{"candidate_id": "CAND_0064326", "profile": {"anonymized_name": "Nisha Pillai", "headline": "Search Engineer | Search, Ranking & Retrieval", "summary": "Machine learning engineer with 7.6 years of experience building ML-powered features in production. Strong background in NLP, recommendation systems, and applied AI; comfortable across the ML stack from feature engineering through deployment. Recently, I shipped our first RAG-based feature this year and now own the eval framework for it. I've spent enough time debugging production ranking issues to know which signals matter and which are noise. My academic background is in CS/ML but my main learning has come from shipping real systems and seeing what holds up under production load. Open to senior IC roles in applied ML or AI engineering, ideally at product companies where I'd own a meaningful piece of the ML stack.", "location": "Gurgaon, Haryana", "country": "India", "years_of_experience": 7.6, "current_title": "Search Engineer", "current_company": "Sarvam AI", "current_company_size": "51-200", "current_industry": "AI/ML"}, "career_history": [{"company": "Sarvam AI", "title": "Search Engineer", "start_date": "2023-11-09", "end_date": null, "duration_months": 31, "is_current": true, "industry": "AI/ML", "company_size": "51-200", "description": "Owned the ranking layer for an e-commerce search product, evolving it from a hand-tuned scoring function to a learning-to-rank model over 9 months. Designed the relevance labeling pipeline (mix of click-through data and explicit human judgments), the feature pipeline, and the training/eval workflow. Most of the work was infrastructure and data quality \u2014 the modeling part was almost the easy bit. Final model improved revenue-per-search by 12%."}, {"company": "Aganitha", "title": "Machine Learning Engineer", "start_date": "2021-11-05", "end_date": "2023-10-26", "duration_months": 24, "is_current": false, "industry": "AI/ML", "company_size": "51-200", "description": "Trained and shipped multiple ranking models for our product's discovery feed using XGBoost and LightGBM. Designed features across three families: content metadata, user behavior signals, and item engagement history. Owned the offline-online correlation analysis that determined which offline metrics actually predicted A/B test outcomes. Worked closely with PMs to define the optimization target (click-through vs. dwell time vs. downstream conversion) \u2014 that work was as important as the modeling itself."}, {"company": "Freshworks", "title": "Machine Learning Engineer", "start_date": "2019-09-17", "end_date": "2021-09-06", "duration_months": 24, "is_current": false, "industry": "SaaS", "company_size": "5001-10000", "description": "Implemented a RAG-based customer support chatbot integrated with our existing ticketing system. Built the document ingestion pipeline (chunking, embedding via OpenAI embeddings, storing in Pinecone) and the answer-generation layer (initially GPT-4, then a fine-tuned smaller model for cost control). Designed the evaluation framework with both automatic metrics (BLEU, ROUGE) and human-in-the-loop quality scores. Deployment cut average ticket resolution time by 31% for the supported categories."}, {"company": "Apple", "title": "Machine Learning Engineer", "start_date": "2018-09-08", "end_date": "2019-09-03", "duration_months": 12, "is_current": false, "industry": "Consumer Electronics", "company_size": "10001+", "description": "Owned the ranking layer for an e-commerce search product, evolving it from a hand-tuned scoring function to a learning-to-rank model over 9 months. Designed the relevance labeling pipeline (mix of click-through data and explicit human judgments), the feature pipeline, and the training/eval workflow. Most of the work was infrastructure and data quality \u2014 the modeling part was almost the easy bit. Final model improved revenue-per-search by 12%."}], "education": [{"institution": "COEP Pune", "degree": "B.Tech", "field_of_study": "Computer Science", "start_year": 2016, "end_year": 2020, "grade": "70%", "tier": "tier_2"}], "skills": [{"name": "scikit-learn", "proficiency": "advanced", "endorsements": 40, "duration_months": 32}, {"name": "PyTorch", "proficiency": "expert", "endorsements": 5, "duration_months": 36}, {"name": "Milvus", "proficiency": "advanced", "endorsements": 1, "duration_months": 58}, {"name": "Deep Learning", "proficiency": "expert", "endorsements": 55, "duration_months": 73}, {"name": "Semantic Search", "proficiency": "advanced", "endorsements": 31, "duration_months": 37}, {"name": "Weaviate", "proficiency": "expert", "endorsements": 47, "duration_months": 51}, {"name": "Object Detection", "proficiency": "intermediate", "endorsements": 3, "duration_months": 32}, {"name": "RAG", "proficiency": "expert", "endorsements": 55, "duration_months": 46}, {"name": "Weights & Biases", "proficiency": "advanced", "endorsements": 21, "duration_months": 36}, {"name": "BM25", "proficiency": "advanced", "endorsements": 1, "duration_months": 41}, {"name": "Webpack", "proficiency": "beginner", "endorsements": 14, "duration_months": 7}, {"name": "Python", "proficiency": "expert", "endorsements": 25, "duration_months": 60}, {"name": "QLoRA", "proficiency": "expert", "endorsements": 2, "duration_months": 76}, {"name": "Reinforcement Learning", "proficiency": "intermediate", "endorsements": 10, "duration_months": 21}], "certifications": [], "languages": [{"language": "English", "proficiency": "native"}, {"language": "Hindi", "proficiency": "conversational"}], "redrob_signals": {"profile_completeness_score": 54.1, "signup_date": "2024-05-09", "last_active_date": "2026-05-21", "open_to_work_flag": true, "profile_views_received_30d": 59, "applications_submitted_30d": 14, "recruiter_response_rate": 0.94, "avg_response_time_hours": 13.5, "skill_assessment_scores": {"scikit-learn": 60.5, "PyTorch": 71.9, "Milvus": 75.5}, "connection_count": 974, "endorsements_received": 21, "notice_period_days": 45, "expected_salary_range_inr_lpa": {"min": 29.2, "max": 38.5}, "preferred_work_mode": "onsite", "willing_to_relocate": false, "github_activity_score": 61.4, "search_appearance_30d": 916, "saved_by_recruiters_30d": 60, "interview_completion_rate": 0.9, "offer_acceptance_rate": -1, "verified_email": true, "verified_phone": true, "linkedin_connected": false}}
{"candidate_id": "CAND_0065195", "profile": {"anonymized_name": "Kiara Mukherjee", "headline": "Search Engineer | Search, Ranking & Retrieval", "summary": "Machine learning engineer with 5.1 years of experience building ML-powered features in production. Strong background in NLP, recommendation systems, and applied AI; comfortable across the ML stack from feature engineering through deployment. Recently, I led the team that migrated our keyword-search-based product to embedding-based retrieval. I care a lot about evaluation rigor \u2014 too many teams ship models without offline benchmarks they trust. My academic background is in CS/ML but my main learning has come from shipping real systems and seeing what holds up under production load. Open to senior IC roles in applied ML or AI engineering, ideally at product companies where I'd own a meaningful piece of the ML stack.", "location": "Kolkata, West Bengal", "country": "India", "years_of_experience": 5.1, "current_title": "Search Engineer", "current_company": "CRED", "current_company_size": "1001-5000", "current_industry": "Fintech"}, "career_history": [{"company": "CRED", "title": "Search Engineer", "start_date": "2022-06-17", "end_date": null, "duration_months": 48, "is_current": true, "industry": "Fintech", "company_size": "1001-5000", "description": "Implemented a RAG-based customer support chatbot integrated with our existing ticketing system. Built the document ingestion pipeline (chunking, embedding via OpenAI embeddings, storing in Pinecone) and the answer-generation layer (initially GPT-4, then a fine-tuned smaller model for cost control). Designed the evaluation framework with both automatic metrics (BLEU, ROUGE) and human-in-the-loop quality scores. Deployment cut average ticket resolution time by 31% for the supported categories."}, {"company": "Google", "title": "Senior Data Scientist", "start_date": "2021-05-23", "end_date": "2022-06-17", "duration_months": 13, "is_current": false, "industry": "Internet", "company_size": "10001+", "description": "Built and operated production ML pipelines using MLflow for experiment tracking, Kubeflow for orchestration, and our internal feature store. My main project was a churn prediction model that's now used by the customer success team to prioritize outreach. Designed the model monitoring stack: data drift detection, prediction distribution checks, and alerting. Mentored a junior engineer through their first end-to-end ML project last year."}], "education": [{"institution": "IIT Bombay", "degree": "M.Sc", "field_of_study": "Artificial Intelligence", "start_year": 2013, "end_year": 2016, "grade": "7.09 CGPA", "tier": "tier_1"}], "skills": [{"name": "LLMs", "proficiency": "expert", "endorsements": 34, "duration_months": 59}, {"name": "BentoML", "proficiency": "intermediate", "endorsements": 2, "duration_months": 35}, {"name": "QLoRA", "proficiency": "expert", "endorsements": 24, "duration_months": 93}, {"name": "scikit-learn", "proficiency": "expert", "endorsements": 19, "duration_months": 74}, {"name": "Diffusion Models", "proficiency": "advanced", "endorsements": 47, "duration_months": 23}, {"name": "Elasticsearch", "proficiency": "expert", "endorsements": 31, "duration_months": 89}, {"name": "Hugging Face Transformers", "proficiency": "advanced", "endorsements": 60, "duration_months": 29}, {"name": "Qdrant", "proficiency": "expert", "endorsements": 13, "duration_months": 94}, {"name": "Django", "proficiency": "intermediate", "endorsements": 15, "duration_months": 24}, {"name": "MLOps", "proficiency": "advanced", "endorsements": 22, "duration_months": 35}, {"name": "pgvector", "proficiency": "expert", "endorsements": 18, "duration_months": 55}, {"name": "Learning to Rank", "proficiency": "advanced", "endorsements": 25, "duration_months": 52}, {"name": "LlamaIndex", "proficiency": "expert", "endorsements": 51, "duration_months": 44}], "certifications": [], "languages": [{"language": "English", "proficiency": "professional"}, {"language": "Hindi", "proficiency": "conversational"}], "redrob_signals": {"profile_completeness_score": 56.4, "signup_date": "2025-07-02", "last_active_date": "2026-05-24", "open_to_work_flag": true, "profile_views_received_30d": 168, "applications_submitted_30d": 2, "recruiter_response_rate": 0.8, "avg_response_time_hours": 22.7, "skill_assessment_scores": {"LLMs": 60.6}, "connection_count": 156, "endorsements_received": 47, "notice_period_days": 45, "expected_salary_range_inr_lpa": {"min": 42.4, "max": 62.4}, "preferred_work_mode": "flexible", "willing_to_relocate": false, "github_activity_score": 87.3, "search_appearance_30d": 1027, "saved_by_recruiters_30d": 13, "interview_completion_rate": 0.91, "offer_acceptance_rate": 0.43, "verified_email": true, "verified_phone": true, "linkedin_connected": true}}
{"candidate_id": "CAND_0046525", "profile": {"anonymized_name": "Tanvi Mukherjee", "headline": "Senior Machine Learning Engineer | Building AI-native search & ranking systems", "summary": "Senior AI engineer with 6.1 years of hands-on experience building production ML systems, with a focus on search, retrieval, and ranking. Most recently, I designed the company's first hybrid retrieval system combining BM25 with dense vector recall, handling peak QPS of 8K with sub-200ms p95. My day-to-day work spans embedding model selection and fine-tuning, hybrid retrieval architecture, learning-to-rank, behavioral-signal integration, and the offline/online evaluation that ties it all together. I've shipped systems in both early-stage product companies and at larger scale, and I've spent enough time on both that I know which tradeoffs apply where. I care more about shipping a working system in 6 weeks than a theoretically perfect one in 6 months. Currently exploring my next move \u2014 looking for senior IC or tech-lead roles where I can own the intelligence layer end-to-end.", "location": "Pune, Maharashtra", "country": "India", "years_of_experience": 6.1, "current_title": "Senior Machine Learning Engineer", "current_company": "Genpact AI", "current_company_size": "10001+", "current_industry": "AI Services"}, "career_history": [{"company": "Genpact AI", "title": "Senior Machine Learning Engineer", "start_date": "2022-06-17", "end_date": null, "duration_months": 48, "is_current": true, "industry": "AI Services", "company_size": "10001+", "description": "Led the migration from keyword-based to embedding-based search across a 30M+ candidate corpus over 8 months. Designed three successive ranker variants and ran them in A/B testing alongside the legacy keyword system. The final embedding ranker improved recruiter engagement metrics by 24% and reduced the average time-to-shortlist by 38%. Most of the engineering effort went into the boring infrastructure: index versioning, embedding versioning, rollback paths, and the dashboards that let recruiters trust the new system. Mentored two junior engineers through this rollout."}, {"company": "LinkedIn", "title": "Senior Machine Learning Engineer", "start_date": "2020-03-29", "end_date": "2022-04-18", "duration_months": 25, "is_current": false, "industry": "Internet", "company_size": "10001+", "description": "Built a RAG-based ranking pipeline serving 50M+ queries per month for an internal recruiter-facing search product. The architecture combined BM25 + dense retrieval (BGE embeddings, FAISS HNSW) with an LLM-based re-ranker on the top-50, falling back to a learning-to-rank model when latency budget was tight. Designed the offline evaluation framework from scratch \u2014 NDCG, MRR, recall@K calibrated against online A/B engagement metrics. Drove the migration over 4 months including the recruiter-feedback loop that surfaced reranking edge cases."}], "education": [{"institution": "Manipal Institute of Technology", "degree": "M.S.", "field_of_study": "Computer Engineering", "start_year": 2009, "end_year": 2013, "grade": "9.36 CGPA", "tier": "tier_2"}, {"institution": "IIT Hyderabad", "degree": "M.Sc", "field_of_study": "Information Technology", "start_year": 2017, "end_year": 2020, "grade": "6.85 CGPA", "tier": "tier_1"}], "skills": [{"name": "Elasticsearch", "proficiency": "advanced", "endorsements": 24, "duration_months": 31}, {"name": "Redux", "proficiency": "beginner", "endorsements": 6, "duration_months": 11}, {"name": "LangChain", "proficiency": "advanced", "endorsements": 12, "duration_months": 27}, {"name": "Machine Learning", "proficiency": "expert", "endorsements": 13, "duration_months": 70}, {"name": "LlamaIndex", "proficiency": "expert", "endorsements": 10, "duration_months": 66}, {"name": "Information Retrieval", "proficiency": "advanced", "endorsements": 45, "duration_months": 59}, {"name": "TensorFlow", "proficiency": "advanced", "endorsements": 15, "duration_months": 19}, {"name": "LLMs", "proficiency": "advanced", "endorsements": 4, "duration_months": 56}, {"name": "NLP", "proficiency": "advanced", "endorsements": 9, "duration_months": 38}, {"name": "Sentence Transformers", "proficiency": "expert", "endorsements": 15, "duration_months": 74}, {"name": "YOLO", "proficiency": "advanced", "endorsements": 56, "duration_months": 48}, {"name": "Forecasting", "proficiency": "advanced", "endorsements": 19, "duration_months": 56}, {"name": "pgvector", "proficiency": "advanced", "endorsements": 46, "duration_months": 20}, {"name": "Image Classification", "proficiency": "intermediate", "endorsements": 5, "duration_months": 13}, {"name": "Weights & Biases", "proficiency": "intermediate", "endorsements": 6, "duration_months": 23}, {"name": "TTS", "proficiency": "advanced", "endorsements": 38, "duration_months": 46}, {"name": "Qdrant", "proficiency": "expert", "endorsements": 2, "duration_months": 44}], "certifications": [{"name": "NLP Specialization", "issuer": "Coursera/DeepLearning.AI", "year": 2023}], "languages": [{"language": "English", "proficiency": "professional"}, {"language": "Hindi", "proficiency": "native"}], "redrob_signals": {"profile_completeness_score": 62.8, "signup_date": "2026-01-22", "last_active_date": "2026-05-23", "open_to_work_flag": true, "profile_views_received_30d": 108, "applications_submitted_30d": 7, "recruiter_response_rate": 0.88, "avg_response_time_hours": 12.0, "skill_assessment_scores": {"Elasticsearch": 77.1, "LangChain": 96.5, "Machine Learning": 86.7, "LlamaIndex": 96.1, "Information Retrieval": 66.4}, "connection_count": 1801, "endorsements_received": 239, "notice_period_days": 60, "expected_salary_range_inr_lpa": {"min": 28.4, "max": 51.9}, "preferred_work_mode": "onsite", "willing_to_relocate": true, "github_activity_score": 36.7, "search_appearance_30d": 663, "saved_by_recruiters_30d": 38, "interview_completion_rate": 0.81, "offer_acceptance_rate": 0.48, "verified_email": true, "verified_phone": true, "linkedin_connected": true}}
{"candidate_id": "CAND_0099806", "profile": {"anonymized_name": "Pranav Subramanian", "headline": "AI Engineer | Search, Ranking & Retrieval", "summary": "Machine learning engineer with 4.6 years of experience building ML-powered features in production. Strong background in NLP, recommendation systems, and applied AI; comfortable across the ML stack from feature engineering through deployment. Recently, I led the team that migrated our keyword-search-based product to embedding-based retrieval. I've spent enough time debugging production ranking issues to know which signals matter and which are noise. My academic background is in CS/ML but my main learning has come from shipping real systems and seeing what holds up under production load. Open to senior IC roles in applied ML or AI engineering, ideally at product companies where I'd own a meaningful piece of the ML stack.", "location": "Bhubaneswar, Odisha", "country": "India", "years_of_experience": 4.6, "current_title": "AI Engineer", "current_company": "Mad Street Den", "current_company_size": "201-500", "current_industry": "AI/ML"}, "career_history": [{"company": "Mad Street Den", "title": "AI Engineer", "start_date": "2023-09-10", "end_date": null, "duration_months": 33, "is_current": true, "industry": "AI/ML", "company_size": "201-500", "description": "Trained and shipped multiple ranking models for our product's discovery feed using XGBoost and LightGBM. Designed features across three families: content metadata, user behavior signals, and item engagement history. Owned the offline-online correlation analysis that determined which offline metrics actually predicted A/B test outcomes. Worked closely with PMs to define the optimization target (click-through vs. dwell time vs. downstream conversion) \u2014 that work was as important as the modeling itself."}, {"company": "upGrad", "title": "Machine Learning Engineer", "start_date": "2021-12-05", "end_date": "2023-08-27", "duration_months": 21, "is_current": false, "industry": "EdTech", "company_size": "1001-5000", "description": "Trained and shipped multiple ranking models for our product's discovery feed using XGBoost and LightGBM. Designed features across three families: content metadata, user behavior signals, and item engagement history. Owned the offline-online correlation analysis that determined which offline metrics actually predicted A/B test outcomes. Worked closely with PMs to define the optimization target (click-through vs. dwell time vs. downstream conversion) \u2014 that work was as important as the modeling itself."}], "education": [{"institution": "IIT Roorkee", "degree": "M.Tech", "field_of_study": "Computer Engineering", "start_year": 2016, "end_year": 2019, "grade": "68%", "tier": "tier_1"}], "skills": [{"name": "LoRA", "proficiency": "expert", "endorsements": 6, "duration_months": 44}, {"name": "OpenCV", "proficiency": "intermediate", "endorsements": 1, "duration_months": 14}, {"name": "Statistical Modeling", "proficiency": "advanced", "endorsements": 18, "duration_months": 21}, {"name": "RAG", "proficiency": "advanced", "endorsements": 9, "duration_months": 52}, {"name": "Agile", "proficiency": "intermediate", "endorsements": 14, "duration_months": 23}, {"name": "Prompt Engineering", "proficiency": "expert", "endorsements": 37, "duration_months": 94}, {"name": "Speech Recognition", "proficiency": "intermediate", "endorsements": 15, "duration_months": 34}, {"name": "Reinforcement Learning", "proficiency": "intermediate", "endorsements": 0, "duration_months": 34}, {"name": "pgvector", "proficiency": "advanced", "endorsements": 30, "duration_months": 18}, {"name": "FAISS", "proficiency": "expert", "endorsements": 26, "duration_months": 58}, {"name": "Embeddings", "proficiency": "expert", "endorsements": 14, "duration_months": 89}, {"name": "Sentence Transformers", "proficiency": "advanced", "endorsements": 7, "duration_months": 19}, {"name": "Weaviate", "proficiency": "advanced", "endorsements": 27, "duration_months": 27}, {"name": "Time Series", "proficiency": "advanced", "endorsements": 35, "duration_months": 27}, {"name": "dbt", "proficiency": "intermediate", "endorsements": 7, "duration_months": 16}, {"name": "Qdrant", "proficiency": "expert", "endorsements": 58, "duration_months": 63}, {"name": "Elasticsearch", "proficiency": "advanced", "endorsements": 10, "duration_months": 56}, {"name": "BM25", "proficiency": "advanced", "endorsements": 46, "duration_months": 36}], "certifications": [{"name": "LangChain for LLM Application Development", "issuer": "DeepLearning.AI", "year": 2022}, {"name": "Deep Learning Specialization", "issuer": "Coursera/DeepLearning.AI", "year": 2021}], "languages": [{"language": "English", "proficiency": "native"}, {"language": "Hindi", "proficiency": "professional"}], "redrob_signals": {"profile_completeness_score": 90.1, "signup_date": "2024-08-20", "last_active_date": "2026-05-05", "open_to_work_flag": true, "profile_views_received_30d": 129, "applications_submitted_30d": 1, "recruiter_response_rate": 0.76, "avg_response_time_hours": 33.0, "skill_assessment_scores": {"LoRA": 69.0}, "connection_count": 223, "endorsements_received": 70, "notice_period_days": 30, "expected_salary_range_inr_lpa": {"min": 31.8, "max": 45.6}, "preferred_work_mode": "hybrid", "willing_to_relocate": true, "github_activity_score": 86.9, "search_appearance_30d": 401, "saved_by_recruiters_30d": 9, "interview_completion_rate": 0.85, "offer_acceptance_rate": 0.38, "verified_email": true, "verified_phone": true, "linkedin_connected": true}}
{"candidate_id": "CAND_0062247", "profile": {"anonymized_name": "Saanvi Trivedi", "headline": "AI Engineer | ML, NLP, Recommendation Systems", "summary": "Machine learning engineer with 7.3 years of experience building ML-powered features in production. Strong background in NLP, recommendation systems, and applied AI; comfortable across the ML stack from feature engineering through deployment. Recently, I led the team that migrated our keyword-search-based product to embedding-based retrieval. I've spent enough time debugging production ranking issues to know which signals matter and which are noise. My academic background is in CS/ML but my main learning has come from shipping real systems and seeing what holds up under production load. Open to senior IC roles in applied ML or AI engineering, ideally at product companies where I'd own a meaningful piece of the ML stack.", "location": "Kochi, Kerala", "country": "India", "years_of_experience": 7.3, "current_title": "AI Engineer", "current_company": "Google", "current_company_size": "10001+", "current_industry": "Internet"}, "career_history": [{"company": "Google", "title": "AI Engineer", "start_date": "2023-05-13", "end_date": null, "duration_months": 37, "is_current": true, "industry": "Internet", "company_size": "10001+", "description": "Developed a semantic search feature for an internal knowledge base of ~500K documents. Used sentence-transformers (all-MiniLM-L6-v2 initially, later upgraded to bge-base) with FAISS for fast nearest-neighbor retrieval. Designed the query expansion module that handles vocabulary mismatch between user queries and document terms. Reported search-relevance improvement of 35% over the prior Elasticsearch BM25 setup, validated through human relevance judgments."}, {"company": "Dream11", "title": "NLP Engineer", "start_date": "2019-04-04", "end_date": "2023-05-13", "duration_months": 50, "is_current": false, "industry": "Gaming", "company_size": "1001-5000", "description": "Owned the ranking layer for an e-commerce search product, evolving it from a hand-tuned scoring function to a learning-to-rank model over 9 months. Designed the relevance labeling pipeline (mix of click-through data and explicit human judgments), the feature pipeline, and the training/eval workflow. Most of the work was infrastructure and data quality \u2014 the modeling part was almost the easy bit. Final model improved revenue-per-search by 12%."}], "education": [{"institution": "IIT Bombay", "degree": "M.E.", "field_of_study": "Machine Learning", "start_year": 2015, "end_year": 2019, "grade": "8.59 CGPA", "tier": "tier_1"}, {"institution": "COEP Pune", "degree": "B.Sc", "field_of_study": "Information Technology", "start_year": 2005, "end_year": 2009, "grade": "7.53 CGPA", "tier": "tier_2"}], "skills": [{"name": "Image Classification", "proficiency": "advanced", "endorsements": 12, "duration_months": 54}, {"name": "OpenCV", "proficiency": "intermediate", "endorsements": 4, "duration_months": 19}, {"name": "Reinforcement Learning", "proficiency": "intermediate", "endorsements": 15, "duration_months": 34}, {"name": "ASR", "proficiency": "intermediate", "endorsements": 10, "duration_months": 18}, {"name": "Pinecone", "proficiency": "expert", "endorsements": 5, "duration_months": 37}, {"name": "Vector Search", "proficiency": "expert", "endorsements": 43, "duration_months": 75}, {"name": "Qdrant", "proficiency": "expert", "endorsements": 10, "duration_months": 90}, {"name": "RAG", "proficiency": "advanced", "endorsements": 50, "duration_months": 30}, {"name": "Computer Vision", "proficiency": "intermediate", "endorsements": 8, "duration_months": 23}, {"name": "PEFT", "proficiency": "advanced", "endorsements": 58, "duration_months": 45}, {"name": "Speech Recognition", "proficiency": "intermediate", "endorsements": 15, "duration_months": 11}, {"name": "Illustrator", "proficiency": "beginner", "endorsements": 0, "duration_months": 7}, {"name": "Hugging Face Transformers", "proficiency": "expert", "endorsements": 57, "duration_months": 88}, {"name": "Learning to Rank", "proficiency": "advanced", "endorsements": 43, "duration_months": 53}, {"name": "Information Retrieval", "proficiency": "advanced", "endorsements": 3, "duration_months": 33}, {"name": "Deep Learning", "proficiency": "expert", "endorsements": 46, "duration_months": 37}, {"name": "BM25", "proficiency": "expert", "endorsements": 27, "duration_months": 66}], "certifications": [], "languages": [{"language": "English", "proficiency": "native"}, {"language": "Hindi", "proficiency": "conversational"}], "redrob_signals": {"profile_completeness_score": 57.2, "signup_date": "2024-09-05", "last_active_date": "2026-04-23", "open_to_work_flag": true, "profile_views_received_30d": 267, "applications_submitted_30d": 19, "recruiter_response_rate": 0.78, "avg_response_time_hours": 21.0, "skill_assessment_scores": {}, "connection_count": 348, "endorsements_received": 106, "notice_period_days": 30, "expected_salary_range_inr_lpa": {"min": 29.2, "max": 61.4}, "preferred_work_mode": "onsite", "willing_to_relocate": true, "github_activity_score": 52.7, "search_appearance_30d": 839, "saved_by_recruiters_30d": 9, "interview_completion_rate": 0.84, "offer_acceptance_rate": 0.57, "verified_email": true, "verified_phone": true, "linkedin_connected": false}}
{"candidate_id": "CAND_0018499", "profile": {"anonymized_name": "Aarav Trivedi", "headline": "Senior Machine Learning Engineer | Building AI-native search & ranking systems", "summary": "Senior AI engineer with 7.2 years of hands-on experience building production ML systems, with a focus on search, retrieval, and ranking. Most recently, I designed the company's first hybrid retrieval system combining BM25 with dense vector recall, serving 50M+ queries per month. My day-to-day work spans embedding model selection and fine-tuning, hybrid retrieval architecture, learning-to-rank, behavioral-signal integration, and the offline/online evaluation that ties it all together. I've shipped systems in both early-stage product companies and at larger scale, and I've spent enough time on both that I know which tradeoffs apply where. I have strong opinions about when LLMs are the right hammer and when classical IR is \u2014 usually it's both. Currently exploring my next move \u2014 looking for senior IC or tech-lead roles where I can own the intelligence layer end-to-end.", "location": "Noida, Uttar Pradesh", "country": "India", "years_of_experience": 7.2, "current_title": "Senior Machine Learning Engineer", "current_company": "Zomato", "current_company_size": "5001-10000", "current_industry": "Food Delivery"}, "career_history": [{"company": "Zomato", "title": "Senior Machine Learning Engineer", "start_date": "2024-04-07", "end_date": null, "duration_months": 26, "is_current": true, "industry": "Food Delivery", "company_size": "5001-10000", "description": "Built a RAG-based ranking pipeline serving 50M+ queries per month for an internal recruiter-facing search product. The architecture combined BM25 + dense retrieval (BGE embeddings, FAISS HNSW) with an LLM-based re-ranker on the top-50, falling back to a learning-to-rank model when latency budget was tight. Designed the offline evaluation framework from scratch \u2014 NDCG, MRR, recall@K calibrated against online A/B engagement metrics. Drove the migration over 4 months including the recruiter-feedback loop that surfaced reranking edge cases."}, {"company": "Google", "title": "Staff Machine Learning Engineer", "start_date": "2022-10-15", "end_date": "2024-04-07", "duration_months": 18, "is_current": false, "industry": "Internet", "company_size": "10001+", "description": "Built a RAG-based ranking pipeline serving 50M+ queries per month for an internal recruiter-facing search product. The architecture combined BM25 + dense retrieval (BGE embeddings, FAISS HNSW) with an LLM-based re-ranker on the top-50, falling back to a learning-to-rank model when latency budget was tight. Designed the offline evaluation framework from scratch \u2014 NDCG, MRR, recall@K calibrated against online A/B engagement metrics. Drove the migration over 4 months including the recruiter-feedback loop that surfaced reranking edge cases."}, {"company": "Flipkart", "title": "Senior Machine Learning Engineer", "start_date": "2019-04-27", "end_date": "2022-10-08", "duration_months": 42, "is_current": false, "industry": "E-commerce", "company_size": "10001+", "description": "Fine-tuned LLaMA-2-7B and Mistral-7B variants using LoRA and QLoRA for domain-specific candidate-JD matching. Built the data curation pipeline that generated 200K high-quality preference pairs from recruiter labels, plus the eval harness using both ranking metrics and human-quality scores. Deployed the model via BentoML on Kubernetes with sub-200ms p95 latency by quantizing to INT8 and batching at the request level. Cost per inference dropped from $0.04 with GPT-3.5-fallback to under $0.001."}], "education": [{"institution": "Massachusetts Institute of Technology", "degree": "B.Sc", "field_of_study": "Artificial Intelligence", "start_year": 2013, "end_year": 2017, "grade": "6.54 CGPA", "tier": "tier_1"}, {"institution": "NIT Surathkal", "degree": "M.S.", "field_of_study": "Data Science", "start_year": 2017, "end_year": 2021, "grade": "69%", "tier": "tier_1"}], "skills": [{"name": "Deep Learning", "proficiency": "expert", "endorsements": 46, "duration_months": 53}, {"name": "Weaviate", "proficiency": "expert", "endorsements": 6, "duration_months": 88}, {"name": "Recommendation Systems", "proficiency": "expert", "endorsements": 52, "duration_months": 46}, {"name": "scikit-learn", "proficiency": "expert", "endorsements": 58, "duration_months": 93}, {"name": "Diffusion Models", "proficiency": "advanced", "endorsements": 49, "duration_months": 60}, {"name": "Pinecone", "proficiency": "advanced", "endorsements": 31, "duration_months": 54}, {"name": "Information Retrieval", "proficiency": "advanced", "endorsements": 49, "duration_months": 27}, {"name": "Milvus", "proficiency": "expert", "endorsements": 18, "duration_months": 40}, {"name": "QLoRA", "proficiency": "expert", "endorsements": 34, "duration_months": 43}, {"name": "RAG", "proficiency": "expert", "endorsements": 1, "duration_months": 94}, {"name": "Embeddings", "proficiency": "expert", "endorsements": 48, "duration_months": 65}, {"name": "Learning to Rank", "proficiency": "expert", "endorsements": 50, "duration_months": 70}, {"name": "CNN", "proficiency": "intermediate", "endorsements": 9, "duration_months": 16}, {"name": "Go", "proficiency": "beginner", "endorsements": 1, "duration_months": 16}, {"name": "BM25", "proficiency": "advanced", "endorsements": 27, "duration_months": 18}, {"name": "LangChain", "proficiency": "advanced", "endorsements": 15, "duration_months": 53}, {"name": "Weights & Biases", "proficiency": "intermediate", "endorsements": 12, "duration_months": 19}], "certifications": [{"name": "Google Cloud Professional ML Engineer", "issuer": "Google Cloud", "year": 2019}, {"name": "NLP Specialization", "issuer": "Coursera/DeepLearning.AI", "year": 2018}], "languages": [{"language": "English", "proficiency": "professional"}, {"language": "Hindi", "proficiency": "conversational"}], "redrob_signals": {"profile_completeness_score": 98.0, "signup_date": "2025-03-11", "last_active_date": "2026-05-13", "open_to_work_flag": true, "profile_views_received_30d": 75, "applications_submitted_30d": 18, "recruiter_response_rate": 0.61, "avg_response_time_hours": 59.6, "skill_assessment_scores": {"Deep Learning": 94.0, "Weaviate": 72.4}, "connection_count": 1839, "endorsements_received": 189, "notice_period_days": 15, "expected_salary_range_inr_lpa": {"min": 36.8, "max": 56.6}, "preferred_work_mode": "remote", "willing_to_relocate": true, "github_activity_score": 94.8, "search_appearance_30d": 1291, "saved_by_recruiters_30d": 16, "interview_completion_rate": 0.8, "offer_acceptance_rate": 0.75, "verified_email": true, "verified_phone": true, "linkedin_connected": true}}
{"candidate_id": "CAND_0079387", "profile": {"anonymized_name": "Sneha Arora", "headline": "AI Engineer | Applied ML | Building intelligent products", "summary": "Machine learning engineer with 6.9 years of experience building ML-powered features in production. Strong background in NLP, recommendation systems, and applied AI; comfortable across the ML stack from feature engineering through deployment. Recently, I've been the de-facto ML lead on a small team, shipping models across NLP and recsys. I've learned that most retrieval problems are actually evaluation problems in disguise. My academic background is in CS/ML but my main learning has come from shipping real systems and seeing what holds up under production load. Open to senior IC roles in applied ML or AI engineering, ideally at product companies where I'd own a meaningful piece of the ML stack.", "location": "Trivandrum, Kerala", "country": "India", "years_of_experience": 6.9, "current_title": "AI Engineer", "current_company": "Microsoft", "current_company_size": "10001+", "current_industry": "Software"}, "career_history": [{"company": "Microsoft", "title": "AI Engineer", "start_date": "2024-08-05", "end_date": null, "duration_months": 22, "is_current": true, "industry": "Software", "company_size": "10001+", "description": "Built a content recommendation system serving 10M+ users that combined collaborative filtering with content-based ranking. The system uses item-item similarity (via sentence-transformer embeddings) for cold starts and a gradient-boosted model trained on engagement signals for warm users. Most of my time went into the feature pipeline (~200 features) and the A/B testing infrastructure. The launch improved 7-day retention by 6% and time spent per session by 14%."}, {"company": "upGrad", "title": "NLP Engineer", "start_date": "2022-10-15", "end_date": "2024-08-05", "duration_months": 22, "is_current": false, "industry": "EdTech", "company_size": "1001-5000", "description": "Implemented a RAG-based customer support chatbot integrated with our existing ticketing system. Built the document ingestion pipeline (chunking, embedding via OpenAI embeddings, storing in Pinecone) and the answer-generation layer (initially GPT-4, then a fine-tuned smaller model for cost control). Designed the evaluation framework with both automatic metrics (BLEU, ROUGE) and human-in-the-loop quality scores. Deployment cut average ticket resolution time by 31% for the supported categories."}, {"company": "Ola", "title": "Applied ML Engineer", "start_date": "2021-03-24", "end_date": "2022-09-15", "duration_months": 18, "is_current": false, "industry": "Transportation", "company_size": "5001-10000", "description": "Built and operated production ML pipelines using MLflow for experiment tracking, Kubeflow for orchestration, and our internal feature store. My main project was a churn prediction model that's now used by the customer success team to prioritize outreach. Designed the model monitoring stack: data drift detection, prediction distribution checks, and alerting. Mentored a junior engineer through their first end-to-end ML project last year."}, {"company": "BYJU'S", "title": "AI Engineer", "start_date": "2019-09-01", "end_date": "2021-03-24", "duration_months": 19, "is_current": false, "industry": "EdTech", "company_size": "10001+", "description": "Developed a semantic search feature for an internal knowledge base of ~500K documents. Used sentence-transformers (all-MiniLM-L6-v2 initially, later upgraded to bge-base) with FAISS for fast nearest-neighbor retrieval. Designed the query expansion module that handles vocabulary mismatch between user queries and document terms. Reported search-relevance improvement of 35% over the prior Elasticsearch BM25 setup, validated through human relevance judgments."}], "education": [{"institution": "IIIT Hyderabad", "degree": "M.Sc", "field_of_study": "Computer Science", "start_year": 2010, "end_year": 2013, "grade": "8.47 CGPA", "tier": "tier_1"}, {"institution": "PES University", "degree": "B.E.", "field_of_study": "Data Science", "start_year": 2002, "end_year": 2007, "grade": "7.46 CGPA", "tier": "tier_2"}], "skills": [{"name": "scikit-learn", "proficiency": "expert", "endorsements": 53, "duration_months": 37}, {"name": "Recommendation Systems", "proficiency": "advanced", "endorsements": 37, "duration_months": 59}, {"name": "Python", "proficiency": "expert", "endorsements": 14, "duration_months": 51}, {"name": "Time Series", "proficiency": "advanced", "endorsements": 54, "duration_months": 50}, {"name": "Sentence Transformers", "proficiency": "expert", "endorsements": 45, "duration_months": 72}, {"name": "JavaScript", "proficiency": "intermediate", "endorsements": 8, "duration_months": 32}, {"name": "Illustrator", "proficiency": "intermediate", "endorsements": 9, "duration_months": 22}, {"name": "Vector Search", "proficiency": "expert", "endorsements": 18, "duration_months": 83}, {"name": "Next.js", "proficiency": "intermediate", "endorsements": 12, "duration_months": 28}, {"name": "OpenSearch", "proficiency": "expert", "endorsements": 16, "duration_months": 87}, {"name": "YOLO", "proficiency": "intermediate", "endorsements": 15, "duration_months": 36}, {"name": "Reinforcement Learning", "proficiency": "intermediate", "endorsements": 9, "duration_months": 24}, {"name": "QLoRA", "proficiency": "expert", "endorsements": 41, "duration_months": 83}, {"name": "LoRA", "proficiency": "expert", "endorsements": 10, "duration_months": 85}, {"name": "BM25", "proficiency": "advanced", "endorsements": 0, "duration_months": 40}, {"name": "Learning to Rank", "proficiency": "advanced", "endorsements": 13, "duration_months": 26}, {"name": "Haystack", "proficiency": "expert", "endorsements": 60, "duration_months": 37}, {"name": "BentoML", "proficiency": "advanced", "endorsements": 41, "duration_months": 44}, {"name": "ASR", "proficiency": "intermediate", "endorsements": 2, "duration_months": 10}, {"name": "Kubeflow", "proficiency": "intermediate", "endorsements": 7, "duration_months": 24}], "certifications": [], "languages": [{"language": "English", "proficiency": "professional"}, {"language": "Hindi", "proficiency": "native"}], "redrob_signals": {"profile_completeness_score": 90.2, "signup_date": "2025-02-16", "last_active_date": "2026-04-25", "open_to_work_flag": true, "profile_views_received_30d": 241, "applications_submitted_30d": 4, "recruiter_response_rate": 0.81, "avg_response_time_hours": 29.1, "skill_assessment_scores": {"scikit-learn": 66.5, "Recommendation Systems": 84.4, "Python": 57.8, "Time Series": 53.2, "Sentence Transformers": 85.8}, "connection_count": 1008, "endorsements_received": 100, "notice_period_days": 30, "expected_salary_range_inr_lpa": {"min": 39.8, "max": 54.7}, "preferred_work_mode": "hybrid", "willing_to_relocate": false, "github_activity_score": 64.1, "search_appearance_30d": 189, "saved_by_recruiters_30d": 22, "interview_completion_rate": 0.9, "offer_acceptance_rate": 0.5, "verified_email": true, "verified_phone": true, "linkedin_connected": true}}
{"candidate_id": "CAND_0052682", "profile": {"anonymized_name": "Ira Mukherjee", "headline": "NLP Engineer | Applied ML | Building intelligent products", "summary": "Machine learning engineer with 6.6 years of experience building ML-powered features in production. Strong background in NLP, recommendation systems, and applied AI; comfortable across the ML stack from feature engineering through deployment. Recently, I've been the de-facto ML lead on a small team, shipping models across NLP and recsys. I care a lot about evaluation rigor \u2014 too many teams ship models without offline benchmarks they trust. My academic background is in CS/ML but my main learning has come from shipping real systems and seeing what holds up under production load. Open to senior IC roles in applied ML or AI engineering, ideally at product companies where I'd own a meaningful piece of the ML stack.", "location": "Vizag, Andhra Pradesh", "country": "India", "years_of_experience": 6.6, "current_title": "NLP Engineer", "current_company": "Aganitha", "current_company_size": "51-200", "current_industry": "AI/ML"}, "career_history": [{"company": "Aganitha", "title": "NLP Engineer", "start_date": "2022-08-16", "end_date": null, "duration_months": 46, "is_current": true, "industry": "AI/ML", "company_size": "51-200", "description": "Built a content recommendation system serving 10M+ users that combined collaborative filtering with content-based ranking. The system uses item-item similarity (via sentence-transformer embeddings) for cold starts and a gradient-boosted model trained on engagement signals for warm users. Most of my time went into the feature pipeline (~200 features) and the A/B testing infrastructure. The launch improved 7-day retention by 6% and time spent per session by 14%."}, {"company": "Salesforce", "title": "NLP Engineer", "start_date": "2019-10-31", "end_date": "2022-06-17", "duration_months": 32, "is_current": false, "industry": "Software", "company_size": "10001+", "description": "Implemented a RAG-based customer support chatbot integrated with our existing ticketing system. Built the document ingestion pipeline (chunking, embedding via OpenAI embeddings, storing in Pinecone) and the answer-generation layer (initially GPT-4, then a fine-tuned smaller model for cost control). Designed the evaluation framework with both automatic metrics (BLEU, ROUGE) and human-in-the-loop quality scores. Deployment cut average ticket resolution time by 31% for the supported categories."}], "education": [{"institution": "NIT Warangal", "degree": "M.E.", "field_of_study": "Information Technology", "start_year": 2012, "end_year": 2017, "grade": "6.63 CGPA", "tier": "tier_1"}, {"institution": "IIT Guwahati", "degree": "Ph.D", "field_of_study": "Computer Science", "start_year": 2014, "end_year": 2017, "grade": "8.49 CGPA", "tier": "tier_1"}], "skills": [{"name": "Statistical Modeling", "proficiency": "intermediate", "endorsements": 1, "duration_months": 17}, {"name": "QLoRA", "proficiency": "expert", "endorsements": 59, "duration_months": 76}, {"name": "Semantic Search", "proficiency": "expert", "endorsements": 38, "duration_months": 89}, {"name": "FAISS", "proficiency": "expert", "endorsements": 2, "duration_months": 76}, {"name": "PyTorch", "proficiency": "expert", "endorsements": 20, "duration_months": 43}, {"name": "gRPC", "proficiency": "intermediate", "endorsements": 10, "duration_months": 27}, {"name": "LLMs", "proficiency": "expert", "endorsements": 9, "duration_months": 55}, {"name": "Excel", "proficiency": "beginner", "endorsements": 9, "duration_months": 16}, {"name": "TTS", "proficiency": "advanced", "endorsements": 23, "duration_months": 41}, {"name": "Six Sigma", "proficiency": "intermediate", "endorsements": 0, "duration_months": 20}, {"name": "Data Science", "proficiency": "advanced", "endorsements": 33, "duration_months": 26}, {"name": "Forecasting", "proficiency": "advanced", "endorsements": 42, "duration_months": 43}, {"name": "Python", "proficiency": "expert", "endorsements": 60, "duration_months": 81}, {"name": "Embeddings", "proficiency": "expert", "endorsements": 48, "duration_months": 50}, {"name": "Fine-tuning LLMs", "proficiency": "expert", "endorsements": 7, "duration_months": 68}], "certifications": [{"name": "Google Cloud Professional ML Engineer", "issuer": "Google Cloud", "year": 2019}], "languages": [{"language": "English", "proficiency": "native"}, {"language": "Hindi", "proficiency": "professional"}], "redrob_signals": {"profile_completeness_score": 85.4, "signup_date": "2025-09-21", "last_active_date": "2026-03-28", "open_to_work_flag": true, "profile_views_received_30d": 290, "applications_submitted_30d": 21, "recruiter_response_rate": 0.88, "avg_response_time_hours": 34.2, "skill_assessment_scores": {"QLoRA": 65.8, "Semantic Search": 53.3}, "connection_count": 1100, "endorsements_received": 128, "notice_period_days": 30, "expected_salary_range_inr_lpa": {"min": 39.7, "max": 54.5}, "preferred_work_mode": "onsite", "willing_to_relocate": false, "github_activity_score": 72.4, "search_appearance_30d": 850, "saved_by_recruiters_30d": 16, "interview_completion_rate": 0.88, "offer_acceptance_rate": 0.85, "verified_email": true, "verified_phone": true, "linkedin_connected": true}}
{"candidate_id": "CAND_0055905", "profile": {"anonymized_name": "Anika Rao", "headline": "Senior Machine Learning Engineer | LLMs, RAG, Vector Search | ex-Top Tech", "summary": "Senior AI engineer with 8.1 years of hands-on experience building production ML systems, with a focus on search, retrieval, and ranking. Most recently, I led the migration from keyword-based ranking to a learning-to-rank model with embedded behavioral signals, serving 50M+ queries per month. My day-to-day work spans embedding model selection and fine-tuning, hybrid retrieval architecture, learning-to-rank, behavioral-signal integration, and the offline/online evaluation that ties it all together. I've shipped systems in both early-stage product companies and at larger scale, and I've spent enough time on both that I know which tradeoffs apply where. I care more about shipping a working system in 6 weeks than a theoretically perfect one in 6 months. Currently exploring my next move \u2014 looking for senior IC or tech-lead roles where I can own the intelligence layer end-to-end.", "location": "London", "country": "UK", "years_of_experience": 8.1, "current_title": "Senior Machine Learning Engineer", "current_company": "Flipkart", "current_company_size": "10001+", "current_industry": "E-commerce"}, "career_history": [{"company": "Flipkart", "title": "Senior Machine Learning Engineer", "start_date": "2025-04-02", "end_date": null, "duration_months": 14, "is_current": true, "industry": "E-commerce", "company_size": "10001+", "description": "Owned the design and rollout of a large-scale semantic search system serving an internal corpus of 35M+ items. Migrated the existing BM25-only retrieval to a hybrid setup combining sparse and dense vectors (sentence-transformers, MPNet-base initially, later fine-tuned BGE-large for our domain). The new system reduced p95 retrieval latency by 60% while improving NDCG@10 by 18% on our held-out eval set. Spent substantial time on the boring-but-critical parts: incremental index refresh, embedding drift monitoring, online/offline metric correlation. Led a team of 4 engineers across the rollout."}, {"company": "Uber", "title": "Senior AI Engineer", "start_date": "2022-03-19", "end_date": "2025-04-02", "duration_months": 37, "is_current": false, "industry": "Transportation", "company_size": "10001+", "description": "Fine-tuned LLaMA-2-7B and Mistral-7B variants using LoRA and QLoRA for domain-specific candidate-JD matching. Built the data curation pipeline that generated 200K high-quality preference pairs from recruiter labels, plus the eval harness using both ranking metrics and human-quality scores. Deployed the model via BentoML on Kubernetes with sub-200ms p95 latency by quantizing to INT8 and batching at the request level. Cost per inference dropped from $0.04 with GPT-3.5-fallback to under $0.001."}, {"company": "Rephrase.ai", "title": "Senior Applied Scientist", "start_date": "2018-05-09", "end_date": "2022-01-18", "duration_months": 45, "is_current": false, "industry": "AI/ML", "company_size": "11-50", "description": "Built a RAG-based ranking pipeline serving 50M+ queries per month for an internal recruiter-facing search product. The architecture combined BM25 + dense retrieval (BGE embeddings, FAISS HNSW) with an LLM-based re-ranker on the top-50, falling back to a learning-to-rank model when latency budget was tight. Designed the offline evaluation framework from scratch \u2014 NDCG, MRR, recall@K calibrated against online A/B engagement metrics. Drove the migration over 4 months including the recruiter-feedback loop that surfaced reranking edge cases."}], "education": [{"institution": "Anna University", "degree": "B.Tech", "field_of_study": "Computer Engineering", "start_year": 2009, "end_year": 2013, "grade": "7.12 CGPA", "tier": "tier_2"}, {"institution": "IIT Kharagpur", "degree": "M.S.", "field_of_study": "Data Science", "start_year": 2002, "end_year": 2005, "grade": "9.14 CGPA", "tier": "tier_1"}], "skills": [{"name": "Elasticsearch", "proficiency": "expert", "endorsements": 23, "duration_months": 80}, {"name": "ASR", "proficiency": "advanced", "endorsements": 56, "duration_months": 41}, {"name": "Hugging Face Transformers", "proficiency": "advanced", "endorsements": 54, "duration_months": 33}, {"name": "Haystack", "proficiency": "advanced", "endorsements": 6, "duration_months": 26}, {"name": "Speech Recognition", "proficiency": "advanced", "endorsements": 20, "duration_months": 57}, {"name": "LangChain", "proficiency": "expert", "endorsements": 33, "duration_months": 70}, {"name": "Python", "proficiency": "expert", "endorsements": 34, "duration_months": 45}, {"name": "LLMs", "proficiency": "advanced", "endorsements": 50, "duration_months": 43}, {"name": "OpenSearch", "proficiency": "expert", "endorsements": 57, "duration_months": 56}, {"name": "Fine-tuning LLMs", "proficiency": "advanced", "endorsements": 57, "duration_months": 50}, {"name": "Information Retrieval", "proficiency": "advanced", "endorsements": 8, "duration_months": 60}, {"name": "Embeddings", "proficiency": "expert", "endorsements": 17, "duration_months": 75}, {"name": "Vector Search", "proficiency": "expert", "endorsements": 1, "duration_months": 93}], "certifications": [], "languages": [{"language": "English", "proficiency": "professional"}, {"language": "Hindi", "proficiency": "professional"}], "redrob_signals": {"profile_completeness_score": 56.7, "signup_date": "2024-11-14", "last_active_date": "2026-05-17", "open_to_work_flag": true, "profile_views_received_30d": 196, "applications_submitted_30d": 9, "recruiter_response_rate": 0.87, "avg_response_time_hours": 11.3, "skill_assessment_scores": {"Elasticsearch": 83.0, "ASR": 69.5, "Hugging Face Transformers": 64.8, "Haystack": 61.8}, "connection_count": 916, "endorsements_received": 209, "notice_period_days": 30, "expected_salary_range_inr_lpa": {"min": 38.9, "max": 64.1}, "preferred_work_mode": "remote", "willing_to_relocate": false, "github_activity_score": -1, "search_appearance_30d": 392, "saved_by_recruiters_30d": 13, "interview_completion_rate": 0.67, "offer_acceptance_rate": 0.93, "verified_email": true, "verified_phone": true, "linkedin_connected": true}}
{"candidate_id": "CAND_0044222", "profile": {"anonymized_name": "Dev Shah", "headline": "AI Engineer | ML, NLP, Recommendation Systems", "summary": "Machine learning engineer with 7.7 years of experience building ML-powered features in production. Strong background in NLP, recommendation systems, and applied AI; comfortable across the ML stack from feature engineering through deployment. Recently, I led the team that migrated our keyword-search-based product to embedding-based retrieval. I care a lot about evaluation rigor \u2014 too many teams ship models without offline benchmarks they trust. My academic background is in CS/ML but my main learning has come from shipping real systems and seeing what holds up under production load. Open to senior IC roles in applied ML or AI engineering, ideally at product companies where I'd own a meaningful piece of the ML stack.", "location": "Vizag, Andhra Pradesh", "country": "India", "years_of_experience": 7.7, "current_title": "AI Engineer", "current_company": "PolicyBazaar", "current_company_size": "5001-10000", "current_industry": "Insurance Tech"}, "career_history": [{"company": "PolicyBazaar", "title": "AI Engineer", "start_date": "2022-05-18", "end_date": null, "duration_months": 49, "is_current": true, "industry": "Insurance Tech", "company_size": "5001-10000", "description": "Developed a semantic search feature for an internal knowledge base of ~500K documents. Used sentence-transformers (all-MiniLM-L6-v2 initially, later upgraded to bge-base) with FAISS for fast nearest-neighbor retrieval. Designed the query expansion module that handles vocabulary mismatch between user queries and document terms. Reported search-relevance improvement of 35% over the prior Elasticsearch BM25 setup, validated through human relevance judgments."}, {"company": "InMobi", "title": "AI Engineer", "start_date": "2018-11-05", "end_date": "2022-05-18", "duration_months": 43, "is_current": false, "industry": "AdTech", "company_size": "1001-5000", "description": "Trained and shipped multiple ranking models for our product's discovery feed using XGBoost and LightGBM. Designed features across three families: content metadata, user behavior signals, and item engagement history. Owned the offline-online correlation analysis that determined which offline metrics actually predicted A/B test outcomes. Worked closely with PMs to define the optimization target (click-through vs. dwell time vs. downstream conversion) \u2014 that work was as important as the modeling itself."}], "education": [{"institution": "IISc Bangalore", "degree": "M.Sc", "field_of_study": "Machine Learning", "start_year": 2017, "end_year": 2021, "grade": "7.28 CGPA", "tier": "tier_1"}], "skills": [{"name": "Vector Search", "proficiency": "advanced", "endorsements": 16, "duration_months": 34}, {"name": "LlamaIndex", "proficiency": "expert", "endorsements": 15, "duration_months": 82}, {"name": "OpenSearch", "proficiency": "expert", "endorsements": 34, "duration_months": 75}, {"name": "Java", "proficiency": "intermediate", "endorsements": 9, "duration_months": 14}, {"name": "Qdrant", "proficiency": "advanced", "endorsements": 31, "duration_months": 26}, {"name": "Feature Engineering", "proficiency": "advanced", "endorsements": 52, "duration_months": 60}, {"name": "Time Series", "proficiency": "intermediate", "endorsements": 6, "duration_months": 33}, {"name": "Weaviate", "proficiency": "advanced", "endorsements": 22, "duration_months": 18}, {"name": "Content Writing", "proficiency": "intermediate", "endorsements": 4, "duration_months": 12}, {"name": "NLP", "proficiency": "advanced", "endorsements": 36, "duration_months": 27}, {"name": "BM25", "proficiency": "advanced", "endorsements": 18, "duration_months": 24}, {"name": "Deep Learning", "proficiency": "expert", "endorsements": 39, "duration_months": 42}, {"name": "LangChain", "proficiency": "expert", "endorsements": 7, "duration_months": 90}, {"name": "Hugging Face Transformers", "proficiency": "expert", "endorsements": 18, "duration_months": 68}, {"name": "Fine-tuning LLMs", "proficiency": "advanced", "endorsements": 45, "duration_months": 43}, {"name": "ASR", "proficiency": "intermediate", "endorsements": 8, "duration_months": 17}, {"name": "Diffusion Models", "proficiency": "intermediate", "endorsements": 0, "duration_months": 31}, {"name": "QLoRA", "proficiency": "advanced", "endorsements": 8, "duration_months": 60}, {"name": "Hadoop", "proficiency": "intermediate", "endorsements": 5, "duration_months": 30}], "certifications": [{"name": "Deep Learning Specialization", "issuer": "Coursera/DeepLearning.AI", "year": 2023}], "languages": [{"language": "English", "proficiency": "native"}, {"language": "Hindi", "proficiency": "conversational"}], "redrob_signals": {"profile_completeness_score": 71.3, "signup_date": "2025-02-01", "last_active_date": "2026-05-15", "open_to_work_flag": true, "profile_views_received_30d": 118, "applications_submitted_30d": 15, "recruiter_response_rate": 0.6, "avg_response_time_hours": 47.0, "skill_assessment_scores": {"Vector Search": 52.4, "LlamaIndex": 84.6}, "connection_count": 550, "endorsements_received": 92, "notice_period_days": 60, "expected_salary_range_inr_lpa": {"min": 42.9, "max": 60.1}, "preferred_work_mode": "hybrid", "willing_to_relocate": false, "github_activity_score": 28.9, "search_appearance_30d": 666, "saved_by_recruiters_30d": 54, "interview_completion_rate": 0.89, "offer_acceptance_rate": 0.3, "verified_email": true, "verified_phone": true, "linkedin_connected": true}}
{"candidate_id": "CAND_0078002", "profile": {"anonymized_name": "Reyansh Sharma", "headline": "Machine Learning Engineer | Search, Ranking & Retrieval", "summary": "Machine learning engineer with 6.3 years of experience building ML-powered features in production. Strong background in NLP, recommendation systems, and applied AI; comfortable across the ML stack from feature engineering through deployment. Recently, I shipped our first RAG-based feature this year and now own the eval framework for it. Along the way I've gotten comfortable with the operational side \u2014 A/B testing, drift monitoring, retraining schedules. My academic background is in CS/ML but my main learning has come from shipping real systems and seeing what holds up under production load. Open to senior IC roles in applied ML or AI engineering, ideally at product companies where I'd own a meaningful piece of the ML stack.", "location": "Coimbatore, Tamil Nadu", "country": "India", "years_of_experience": 6.3, "current_title": "Machine Learning Engineer", "current_company": "Meta", "current_company_size": "10001+", "current_industry": "Internet"}, "career_history": [{"company": "Meta", "title": "Machine Learning Engineer", "start_date": "2023-08-11", "end_date": null, "duration_months": 34, "is_current": true, "industry": "Internet", "company_size": "10001+", "description": "Trained and shipped multiple ranking models for our product's discovery feed using XGBoost and LightGBM. Designed features across three families: content metadata, user behavior signals, and item engagement history. Owned the offline-online correlation analysis that determined which offline metrics actually predicted A/B test outcomes. Worked closely with PMs to define the optimization target (click-through vs. dwell time vs. downstream conversion) \u2014 that work was as important as the modeling itself."}, {"company": "Genpact AI", "title": "Applied ML Engineer", "start_date": "2021-02-15", "end_date": "2023-08-04", "duration_months": 30, "is_current": false, "industry": "AI Services", "company_size": "10001+", "description": "Built a content recommendation system serving 10M+ users that combined collaborative filtering with content-based ranking. The system uses item-item similarity (via sentence-transformer embeddings) for cold starts and a gradient-boosted model trained on engagement signals for warm users. Most of my time went into the feature pipeline (~200 features) and the A/B testing infrastructure. The launch improved 7-day retention by 6% and time spent per session by 14%."}, {"company": "CRED", "title": "Senior Data Scientist", "start_date": "2020-04-07", "end_date": "2021-02-01", "duration_months": 10, "is_current": false, "industry": "Fintech", "company_size": "1001-5000", "description": "Trained and shipped multiple ranking models for our product's discovery feed using XGBoost and LightGBM. Designed features across three families: content metadata, user behavior signals, and item engagement history. Owned the offline-online correlation analysis that determined which offline metrics actually predicted A/B test outcomes. Worked closely with PMs to define the optimization target (click-through vs. dwell time vs. downstream conversion) \u2014 that work was as important as the modeling itself."}], "education": [{"institution": "NIT Trichy", "degree": "M.E.", "field_of_study": "Artificial Intelligence", "start_year": 2012, "end_year": 2016, "grade": "84%", "tier": "tier_1"}, {"institution": "IIT Guwahati", "degree": "B.E.", "field_of_study": "Machine Learning", "start_year": 2014, "end_year": 2017, "grade": "7.90 CGPA", "tier": "tier_1"}], "skills": [{"name": "Statistical Modeling", "proficiency": "advanced", "endorsements": 34, "duration_months": 50}, {"name": "pgvector", "proficiency": "expert", "endorsements": 2, "duration_months": 60}, {"name": "Flask", "proficiency": "intermediate", "endorsements": 14, "duration_months": 34}, {"name": "FastAPI", "proficiency": "beginner", "endorsements": 11, "duration_months": 18}, {"name": "TensorFlow", "proficiency": "expert", "endorsements": 3, "duration_months": 80}, {"name": "NLP", "proficiency": "expert", "endorsements": 40, "duration_months": 50}, {"name": "BentoML", "proficiency": "advanced", "endorsements": 32, "duration_months": 20}, {"name": "scikit-learn", "proficiency": "advanced", "endorsements": 22, "duration_months": 30}, {"name": "Time Series", "proficiency": "advanced", "endorsements": 23, "duration_months": 42}, {"name": "Hugging Face Transformers", "proficiency": "expert", "endorsements": 35, "duration_months": 55}, {"name": "Weaviate", "proficiency": "advanced", "endorsements": 11, "duration_months": 19}, {"name": "LlamaIndex", "proficiency": "expert", "endorsements": 27, "duration_months": 69}, {"name": "Deep Learning", "proficiency": "advanced", "endorsements": 11, "duration_months": 33}, {"name": "QLoRA", "proficiency": "expert", "endorsements": 59, "duration_months": 90}, {"name": "LangChain", "proficiency": "advanced", "endorsements": 0, "duration_months": 20}, {"name": "Haystack", "proficiency": "expert", "endorsements": 45, "duration_months": 45}], "certifications": [], "languages": [{"language": "English", "proficiency": "professional"}, {"language": "Hindi", "proficiency": "native"}], "redrob_signals": {"profile_completeness_score": 85.8, "signup_date": "2024-08-02", "last_active_date": "2026-04-21", "open_to_work_flag": true, "profile_views_received_30d": 173, "applications_submitted_30d": 12, "recruiter_response_rate": 0.86, "avg_response_time_hours": 54.2, "skill_assessment_scores": {"Statistical Modeling": 68.7, "pgvector": 80.2}, "connection_count": 1292, "endorsements_received": 53, "notice_period_days": 60, "expected_salary_range_inr_lpa": {"min": 35.3, "max": 52.2}, "preferred_work_mode": "hybrid", "willing_to_relocate": false, "github_activity_score": 50.6, "search_appearance_30d": 798, "saved_by_recruiters_30d": 6, "interview_completion_rate": 0.86, "offer_acceptance_rate": 0.41, "verified_email": true, "verified_phone": true, "linkedin_connected": true}}
{"candidate_id": "CAND_0076163", "profile": {"anonymized_name": "Nikhil Mittal", "headline": "NLP Engineer | Applied ML | Building intelligent products", "summary": "Machine learning engineer with 6.9 years of experience building ML-powered features in production. Strong background in NLP, recommendation systems, and applied AI; comfortable across the ML stack from feature engineering through deployment. Recently, I shipped our first RAG-based feature this year and now own the eval framework for it. I've spent enough time debugging production ranking issues to know which signals matter and which are noise. My academic background is in CS/ML but my main learning has come from shipping real systems and seeing what holds up under production load. Open to senior IC roles in applied ML or AI engineering, ideally at product companies where I'd own a meaningful piece of the ML stack.", "location": "Chandigarh, Chandigarh", "country": "India", "years_of_experience": 6.9, "current_title": "NLP Engineer", "current_company": "Ola", "current_company_size": "5001-10000", "current_industry": "Transportation"}, "career_history": [{"company": "Ola", "title": "NLP Engineer", "start_date": "2022-08-16", "end_date": null, "duration_months": 46, "is_current": true, "industry": "Transportation", "company_size": "5001-10000", "description": "Owned the ranking layer for an e-commerce search product, evolving it from a hand-tuned scoring function to a learning-to-rank model over 9 months. Designed the relevance labeling pipeline (mix of click-through data and explicit human judgments), the feature pipeline, and the training/eval workflow. Most of the work was infrastructure and data quality \u2014 the modeling part was almost the easy bit. Final model improved revenue-per-search by 12%."}, {"company": "Zoho", "title": "NLP Engineer", "start_date": "2019-09-01", "end_date": "2022-08-16", "duration_months": 36, "is_current": false, "industry": "SaaS", "company_size": "10001+", "description": "Owned the ranking layer for an e-commerce search product, evolving it from a hand-tuned scoring function to a learning-to-rank model over 9 months. Designed the relevance labeling pipeline (mix of click-through data and explicit human judgments), the feature pipeline, and the training/eval workflow. Most of the work was infrastructure and data quality \u2014 the modeling part was almost the easy bit. Final model improved revenue-per-search by 12%."}], "education": [{"institution": "VJTI Mumbai", "degree": "M.Tech", "field_of_study": "Information Technology", "start_year": 2015, "end_year": 2018, "grade": "8.36 CGPA", "tier": "tier_2"}], "skills": [{"name": "Weaviate", "proficiency": "advanced", "endorsements": 55, "duration_months": 18}, {"name": "LangChain", "proficiency": "expert", "endorsements": 13, "duration_months": 75}, {"name": "YOLO", "proficiency": "intermediate", "endorsements": 11, "duration_months": 34}, {"name": "Reinforcement Learning", "proficiency": "intermediate", "endorsements": 7, "duration_months": 23}, {"name": "Statistical Modeling", "proficiency": "advanced", "endorsements": 46, "duration_months": 27}, {"name": "BM25", "proficiency": "advanced", "endorsements": 12, "duration_months": 57}, {"name": "Semantic Search", "proficiency": "expert", "endorsements": 53, "duration_months": 82}, {"name": "LlamaIndex", "proficiency": "expert", "endorsements": 0, "duration_months": 86}, {"name": "Prompt Engineering", "proficiency": "advanced", "endorsements": 48, "duration_months": 23}, {"name": "PyTorch", "proficiency": "advanced", "endorsements": 40, "duration_months": 38}, {"name": "OpenCV", "proficiency": "intermediate", "endorsements": 4, "duration_months": 27}, {"name": "Sentence Transformers", "proficiency": "advanced", "endorsements": 32, "duration_months": 28}, {"name": "Python", "proficiency": "advanced", "endorsements": 59, "duration_months": 25}, {"name": "Elasticsearch", "proficiency": "expert", "endorsements": 14, "duration_months": 39}, {"name": "ASR", "proficiency": "intermediate", "endorsements": 5, "duration_months": 19}, {"name": "MLOps", "proficiency": "intermediate", "endorsements": 1, "duration_months": 12}, {"name": "OpenSearch", "proficiency": "advanced", "endorsements": 21, "duration_months": 46}], "certifications": [{"name": "Deep Learning Specialization", "issuer": "Coursera/DeepLearning.AI", "year": 2019}, {"name": "AWS Certified Machine Learning Specialty", "issuer": "AWS", "year": 2021}], "languages": [{"language": "English", "proficiency": "native"}, {"language": "Hindi", "proficiency": "professional"}], "redrob_signals": {"profile_completeness_score": 89.7, "signup_date": "2024-05-03", "last_active_date": "2026-05-10", "open_to_work_flag": true, "profile_views_received_30d": 222, "applications_submitted_30d": 5, "recruiter_response_rate": 0.84, "avg_response_time_hours": 56.7, "skill_assessment_scores": {"Weaviate": 78.5, "LangChain": 50.7, "Statistical Modeling": 71.0, "BM25": 91.8, "Semantic Search": 58.2}, "connection_count": 1174, "endorsements_received": 91, "notice_period_days": 60, "expected_salary_range_inr_lpa": {"min": 27.4, "max": 47.6}, "preferred_work_mode": "hybrid", "willing_to_relocate": false, "github_activity_score": 84.6, "search_appearance_30d": 219, "saved_by_recruiters_30d": 62, "interview_completion_rate": 0.72, "offer_acceptance_rate": 0.48, "verified_email": true, "verified_phone": true, "linkedin_connected": true}}
{"candidate_id": "CAND_0050876", "profile": {"anonymized_name": "Vivaan Shah", "headline": "Applied ML Engineer | ML, NLP, Recommendation Systems", "summary": "Machine learning engineer with 6.0 years of experience building ML-powered features in production. Strong background in NLP, recommendation systems, and applied AI; comfortable across the ML stack from feature engineering through deployment. Recently, my main project for the last 18 months has been the recommendation system that powers our discovery feed. I care a lot about evaluation rigor \u2014 too many teams ship models without offline benchmarks they trust. My academic background is in CS/ML but my main learning has come from shipping real systems and seeing what holds up under production load. Open to senior IC roles in applied ML or AI engineering, ideally at product companies where I'd own a meaningful piece of the ML stack.", "location": "Kolkata, West Bengal", "country": "India", "years_of_experience": 6.0, "current_title": "Applied ML Engineer", "current_company": "Freshworks", "current_company_size": "5001-10000", "current_industry": "SaaS"}, "career_history": [{"company": "Freshworks", "title": "Applied ML Engineer", "start_date": "2023-04-13", "end_date": null, "duration_months": 38, "is_current": true, "industry": "SaaS", "company_size": "5001-10000", "description": "Owned the ranking layer for an e-commerce search product, evolving it from a hand-tuned scoring function to a learning-to-rank model over 9 months. Designed the relevance labeling pipeline (mix of click-through data and explicit human judgments), the feature pipeline, and the training/eval workflow. Most of the work was infrastructure and data quality \u2014 the modeling part was almost the easy bit. Final model improved revenue-per-search by 12%."}, {"company": "Yellow.ai", "title": "AI Engineer", "start_date": "2021-04-23", "end_date": "2023-04-13", "duration_months": 24, "is_current": false, "industry": "AI/ML", "company_size": "201-500", "description": "Built a content recommendation system serving 10M+ users that combined collaborative filtering with content-based ranking. The system uses item-item similarity (via sentence-transformer embeddings) for cold starts and a gradient-boosted model trained on engagement signals for warm users. Most of my time went into the feature pipeline (~200 features) and the A/B testing infrastructure. The launch improved 7-day retention by 6% and time spent per session by 14%."}, {"company": "Razorpay", "title": "Recommendation Systems Engineer", "start_date": "2020-06-27", "end_date": "2021-03-24", "duration_months": 9, "is_current": false, "industry": "Fintech", "company_size": "1001-5000", "description": "Built a content recommendation system serving 10M+ users that combined collaborative filtering with content-based ranking. The system uses item-item similarity (via sentence-transformer embeddings) for cold starts and a gradient-boosted model trained on engagement signals for warm users. Most of my time went into the feature pipeline (~200 features) and the A/B testing infrastructure. The launch improved 7-day retention by 6% and time spent per session by 14%."}], "education": [{"institution": "Stanford University", "degree": "M.S.", "field_of_study": "Machine Learning", "start_year": 2010, "end_year": 2013, "grade": "9.24 CGPA", "tier": "tier_1"}, {"institution": "IIT Kharagpur", "degree": "M.E.", "field_of_study": "Information Technology", "start_year": 2013, "end_year": 2017, "grade": "8.92 CGPA", "tier": "tier_1"}], "skills": [{"name": "SQL", "proficiency": "beginner", "endorsements": 9, "duration_months": 15}, {"name": "Qdrant", "proficiency": "advanced", "endorsements": 54, "duration_months": 28}, {"name": "MLOps", "proficiency": "advanced", "endorsements": 33, "duration_months": 21}, {"name": "FAISS", "proficiency": "advanced", "endorsements": 19, "duration_months": 31}, {"name": "scikit-learn", "proficiency": "expert", "endorsements": 3, "duration_months": 85}, {"name": "Weights & Biases", "proficiency": "advanced", "endorsements": 28, "duration_months": 59}, {"name": "LlamaIndex", "proficiency": "advanced", "endorsements": 5, "duration_months": 41}, {"name": "Forecasting", "proficiency": "advanced", "endorsements": 2, "duration_months": 27}, {"name": "Machine Learning", "proficiency": "advanced", "endorsements": 17, "duration_months": 51}, {"name": "OpenSearch", "proficiency": "expert", "endorsements": 3, "duration_months": 59}, {"name": "YOLO", "proficiency": "intermediate", "endorsements": 0, "duration_months": 32}, {"name": "Kubeflow", "proficiency": "intermediate", "endorsements": 4, "duration_months": 36}, {"name": "QLoRA", "proficiency": "expert", "endorsements": 16, "duration_months": 58}, {"name": "Sentence Transformers", "proficiency": "expert", "endorsements": 51, "duration_months": 70}, {"name": "Image Classification", "proficiency": "intermediate", "endorsements": 13, "duration_months": 30}, {"name": "Python", "proficiency": "expert", "endorsements": 2, "duration_months": 66}, {"name": "Prompt Engineering", "proficiency": "expert", "endorsements": 50, "duration_months": 81}, {"name": "PyTorch", "proficiency": "advanced", "endorsements": 38, "duration_months": 29}], "certifications": [{"name": "LangChain for LLM Application Development", "issuer": "DeepLearning.AI", "year": 2018}, {"name": "AWS Certified Machine Learning Specialty", "issuer": "AWS", "year": 2018}], "languages": [{"language": "English", "proficiency": "native"}, {"language": "Hindi", "proficiency": "native"}], "redrob_signals": {"profile_completeness_score": 81.8, "signup_date": "2025-11-21", "last_active_date": "2026-05-26", "open_to_work_flag": true, "profile_views_received_30d": 20, "applications_submitted_30d": 0, "recruiter_response_rate": 0.67, "avg_response_time_hours": 79.5, "skill_assessment_scores": {"Qdrant": 61.0, "MLOps": 55.8, "FAISS": 81.1}, "connection_count": 843, "endorsements_received": 69, "notice_period_days": 90, "expected_salary_range_inr_lpa": {"min": 27.8, "max": 49.5}, "preferred_work_mode": "hybrid", "willing_to_relocate": false, "github_activity_score": 86.1, "search_appearance_30d": 42, "saved_by_recruiters_30d": 3, "interview_completion_rate": 0.97, "offer_acceptance_rate": 0.77, "verified_email": true, "verified_phone": true, "linkedin_connected": true}}
{"candidate_id": "CAND_0098846", "profile": {"anonymized_name": "Shreya Saxena", "headline": "AI Engineer | Search, Ranking & Retrieval", "summary": "Machine learning engineer with 7.6 years of experience building ML-powered features in production. Strong background in NLP, recommendation systems, and applied AI; comfortable across the ML stack from feature engineering through deployment. Recently, I've been the de-facto ML lead on a small team, shipping models across NLP and recsys. Along the way I've gotten comfortable with the operational side \u2014 A/B testing, drift monitoring, retraining schedules. My academic background is in CS/ML but my main learning has come from shipping real systems and seeing what holds up under production load. Open to senior IC roles in applied ML or AI engineering, ideally at product companies where I'd own a meaningful piece of the ML stack.", "location": "Indore, Madhya Pradesh", "country": "India", "years_of_experience": 7.6, "current_title": "AI Engineer", "current_company": "upGrad", "current_company_size": "1001-5000", "current_industry": "EdTech"}, "career_history": [{"company": "upGrad", "title": "AI Engineer", "start_date": "2024-05-07", "end_date": null, "duration_months": 25, "is_current": true, "industry": "EdTech", "company_size": "1001-5000", "description": "Owned the ranking layer for an e-commerce search product, evolving it from a hand-tuned scoring function to a learning-to-rank model over 9 months. Designed the relevance labeling pipeline (mix of click-through data and explicit human judgments), the feature pipeline, and the training/eval workflow. Most of the work was infrastructure and data quality \u2014 the modeling part was almost the easy bit. Final model improved revenue-per-search by 12%."}, {"company": "Meesho", "title": "Machine Learning Engineer", "start_date": "2022-09-15", "end_date": "2024-05-07", "duration_months": 20, "is_current": false, "industry": "E-commerce", "company_size": "1001-5000", "description": "Built and operated production ML pipelines using MLflow for experiment tracking, Kubeflow for orchestration, and our internal feature store. My main project was a churn prediction model that's now used by the customer success team to prioritize outreach. Designed the model monitoring stack: data drift detection, prediction distribution checks, and alerting. Mentored a junior engineer through their first end-to-end ML project last year."}, {"company": "Swiggy", "title": "Search Engineer", "start_date": "2021-02-22", "end_date": "2022-09-15", "duration_months": 19, "is_current": false, "industry": "Food Delivery", "company_size": "5001-10000", "description": "Implemented a RAG-based customer support chatbot integrated with our existing ticketing system. Built the document ingestion pipeline (chunking, embedding via OpenAI embeddings, storing in Pinecone) and the answer-generation layer (initially GPT-4, then a fine-tuned smaller model for cost control). Designed the evaluation framework with both automatic metrics (BLEU, ROUGE) and human-in-the-loop quality scores. Deployment cut average ticket resolution time by 31% for the supported categories."}, {"company": "Google", "title": "Search Engineer", "start_date": "2019-01-04", "end_date": "2021-02-22", "duration_months": 26, "is_current": false, "industry": "Internet", "company_size": "10001+", "description": "Built and operated production ML pipelines using MLflow for experiment tracking, Kubeflow for orchestration, and our internal feature store. My main project was a churn prediction model that's now used by the customer success team to prioritize outreach. Designed the model monitoring stack: data drift detection, prediction distribution checks, and alerting. Mentored a junior engineer through their first end-to-end ML project last year."}], "education": [{"institution": "IIT Kanpur", "degree": "Ph.D", "field_of_study": "Machine Learning", "start_year": 2009, "end_year": 2013, "grade": "7.56 CGPA", "tier": "tier_1"}], "skills": [{"name": "YOLO", "proficiency": "intermediate", "endorsements": 10, "duration_months": 20}, {"name": "Kubeflow", "proficiency": "intermediate", "endorsements": 6, "duration_months": 9}, {"name": "Hugging Face Transformers", "proficiency": "expert", "endorsements": 9, "duration_months": 66}, {"name": "Hadoop", "proficiency": "beginner", "endorsements": 7, "duration_months": 4}, {"name": "PEFT", "proficiency": "expert", "endorsements": 47, "duration_months": 47}, {"name": "QLoRA", "proficiency": "expert", "endorsements": 6, "duration_months": 66}, {"name": "Machine Learning", "proficiency": "expert", "endorsements": 9, "duration_months": 92}, {"name": "MLOps", "proficiency": "intermediate", "endorsements": 12, "duration_months": 22}, {"name": "Qdrant", "proficiency": "expert", "endorsements": 51, "duration_months": 95}, {"name": "Haystack", "proficiency": "advanced", "endorsements": 51, "duration_months": 38}, {"name": "LlamaIndex", "proficiency": "expert", "endorsements": 13, "duration_months": 43}, {"name": "TensorFlow", "proficiency": "advanced", "endorsements": 16, "duration_months": 55}, {"name": "Recommendation Systems", "proficiency": "expert", "endorsements": 53, "duration_months": 76}, {"name": "Speech Recognition", "proficiency": "advanced", "endorsements": 7, "duration_months": 23}, {"name": "Photoshop", "proficiency": "intermediate", "endorsements": 10, "duration_months": 12}, {"name": "Azure", "proficiency": "beginner", "endorsements": 6, "duration_months": 17}, {"name": "BentoML", "proficiency": "advanced", "endorsements": 29, "duration_months": 33}, {"name": "TTS", "proficiency": "intermediate", "endorsements": 2, "duration_months": 24}], "certifications": [], "languages": [{"language": "English", "proficiency": "native"}, {"language": "Hindi", "proficiency": "native"}], "redrob_signals": {"profile_completeness_score": 90.0, "signup_date": "2025-09-15", "last_active_date": "2026-04-23", "open_to_work_flag": true, "profile_views_received_30d": 102, "applications_submitted_30d": 13, "recruiter_response_rate": 0.62, "avg_response_time_hours": 12.0, "skill_assessment_scores": {}, "connection_count": 1378, "endorsements_received": 99, "notice_period_days": 45, "expected_salary_range_inr_lpa": {"min": 44.0, "max": 54.7}, "preferred_work_mode": "flexible", "willing_to_relocate": true, "github_activity_score": 86.2, "search_appearance_30d": 747, "saved_by_recruiters_30d": 13, "interview_completion_rate": 0.8, "offer_acceptance_rate": -1, "verified_email": true, "verified_phone": true, "linkedin_connected": true}}
{"candidate_id": "CAND_0043860", "profile": {"anonymized_name": "Pranav Sharma", "headline": "Junior ML Engineer | 6.1 yrs in analytics & ML", "summary": "Data scientist / ML engineer with 6.1 years of experience in applied machine learning. Worked across predictive modeling, NLP, analytics, and lightweight deployment workflows. I've spent the last couple of years building NLP-based classification and information extraction pipelines. I'm strongest at the modeling and analysis side; comfortable with Python, scikit-learn, pandas, and standard MLOps tooling, but I'm still building depth on the engineering and infra side of production ML. I want to grow into senior AI engineering \u2014 get serious about LLMs and retrieval beyond the surface level.", "location": "Bhubaneswar, Odisha", "country": "India", "years_of_experience": 6.1, "current_title": "Junior ML Engineer", "current_company": "Aganitha", "current_company_size": "51-200", "current_industry": "AI/ML"}, "career_history": [{"company": "Aganitha", "title": "Junior ML Engineer", "start_date": "2022-06-17", "end_date": null, "duration_months": 48, "is_current": true, "industry": "AI/ML", "company_size": "51-200", "description": "Built recommendation-style features at a mid-stage startup \u2014 lighter weight than ranking systems at FAANG, but production. Used a combination of collaborative filtering (matrix factorization in implicit-feedback library) and gradient-boosted re-ranking over engagement signals. Pure ML side of the work; production deployment was handled by the platform team."}, {"company": "Nykaa", "title": "Senior Software Engineer (ML)", "start_date": "2020-05-28", "end_date": "2022-06-17", "duration_months": 25, "is_current": false, "industry": "E-commerce", "company_size": "1001-5000", "description": "Built computer vision models for our product's image moderation feature using PyTorch \u2014 fine-tuned ResNet variants on a labeled dataset of ~200K images. Set up the training pipeline (data loading, augmentation, evaluation) and the inference service. Most of my project work has been in CV; I'm now interested in transitioning toward NLP/LLM work but my professional experience there is limited."}], "education": [{"institution": "PES University", "degree": "M.E.", "field_of_study": "Artificial Intelligence", "start_year": 2003, "end_year": 2006, "grade": "90%", "tier": "tier_2"}], "skills": [{"name": "pgvector", "proficiency": "intermediate", "endorsements": 3, "duration_months": 23}, {"name": "Feature Engineering", "proficiency": "intermediate", "endorsements": 8, "duration_months": 35}, {"name": "Object Detection", "proficiency": "intermediate", "endorsements": 6, "duration_months": 29}, {"name": "Semantic Search", "proficiency": "advanced", "endorsements": 49, "duration_months": 44}, {"name": "Information Retrieval", "proficiency": "advanced", "endorsements": 30, "duration_months": 30}, {"name": "Redis", "proficiency": "beginner", "endorsements": 15, "duration_months": 8}, {"name": "OpenCV", "proficiency": "advanced", "endorsements": 7, "duration_months": 60}, {"name": "Deep Learning", "proficiency": "intermediate", "endorsements": 8, "duration_months": 30}, {"name": "Qdrant", "proficiency": "intermediate", "endorsements": 10, "duration_months": 24}, {"name": "PyTorch", "proficiency": "intermediate", "endorsements": 8, "duration_months": 28}, {"name": "Vector Search", "proficiency": "intermediate", "endorsements": 6, "duration_months": 18}, {"name": "Forecasting", "proficiency": "advanced", "endorsements": 3, "duration_months": 33}, {"name": "GANs", "proficiency": "advanced", "endorsements": 39, "duration_months": 51}, {"name": "Diffusion Models", "proficiency": "advanced", "endorsements": 21, "duration_months": 29}], "certifications": [], "languages": [{"language": "English", "proficiency": "native"}, {"language": "Hindi", "proficiency": "professional"}], "redrob_signals": {"profile_completeness_score": 94.6, "signup_date": "2026-05-02", "last_active_date": "2026-05-26", "open_to_work_flag": true, "profile_views_received_30d": 140, "applications_submitted_30d": 3, "recruiter_response_rate": 0.81, "avg_response_time_hours": 7.1, "skill_assessment_scores": {"Semantic Search": 50.3, "Information Retrieval": 83.5, "OpenCV": 78.5}, "connection_count": 1177, "endorsements_received": 105, "notice_period_days": 30, "expected_salary_range_inr_lpa": {"min": 16.5, "max": 32.1}, "preferred_work_mode": "hybrid", "willing_to_relocate": true, "github_activity_score": -1, "search_appearance_30d": 461, "saved_by_recruiters_30d": 33, "interview_completion_rate": 0.94, "offer_acceptance_rate": 0.8, "verified_email": true, "verified_phone": true, "linkedin_connected": false}}
{"candidate_id": "CAND_0024147", "profile": {"anonymized_name": "Zara Bhatia", "headline": "ML Engineer | Data Science & ML enthusiast", "summary": "Data scientist / ML engineer with 5.9 years of experience in applied machine learning. Worked across predictive modeling, NLP, analytics, and lightweight deployment workflows. I've been working on recommendation-style features but lighter on the deep-learning side \u2014 mostly classical methods like collaborative filtering and gradient-boosted models. I'm strongest at the modeling and analysis side; comfortable with Python, scikit-learn, pandas, and standard MLOps tooling, but I'm still building depth on the engineering and infra side of production ML. I want to grow into senior AI engineering \u2014 get serious about LLMs and retrieval beyond the surface level.", "location": "Jaipur, Rajasthan", "country": "India", "years_of_experience": 5.9, "current_title": "ML Engineer", "current_company": "Ola", "current_company_size": "5001-10000", "current_industry": "Transportation"}, "career_history": [{"company": "Ola", "title": "ML Engineer", "start_date": "2023-02-12", "end_date": null, "duration_months": 40, "is_current": true, "industry": "Transportation", "company_size": "5001-10000", "description": "Built recommendation-style features at a mid-stage startup \u2014 lighter weight than ranking systems at FAANG, but production. Used a combination of collaborative filtering (matrix factorization in implicit-feedback library) and gradient-boosted re-ranking over engagement signals. Pure ML side of the work; production deployment was handled by the platform team."}, {"company": "PhonePe", "title": "Data Scientist", "start_date": "2020-08-26", "end_date": "2023-02-12", "duration_months": 30, "is_current": false, "industry": "Fintech", "company_size": "5001-10000", "description": "Built computer vision models for our product's image moderation feature using PyTorch \u2014 fine-tuned ResNet variants on a labeled dataset of ~200K images. Set up the training pipeline (data loading, augmentation, evaluation) and the inference service. Most of my project work has been in CV; I'm now interested in transitioning toward NLP/LLM work but my professional experience there is limited."}], "education": [{"institution": "Thapar University", "degree": "B.E.", "field_of_study": "Computer Science", "start_year": 2001, "end_year": 2005, "grade": "91%", "tier": "tier_2"}], "skills": [{"name": "ASR", "proficiency": "intermediate", "endorsements": 12, "duration_months": 12}, {"name": "Qdrant", "proficiency": "advanced", "endorsements": 51, "duration_months": 35}, {"name": "Kubeflow", "proficiency": "intermediate", "endorsements": 12, "duration_months": 23}, {"name": "Feature Engineering", "proficiency": "advanced", "endorsements": 35, "duration_months": 55}, {"name": "Reinforcement Learning", "proficiency": "intermediate", "endorsements": 13, "duration_months": 9}, {"name": "Recommendation Systems", "proficiency": "intermediate", "endorsements": 7, "duration_months": 33}, {"name": "OpenSearch", "proficiency": "intermediate", "endorsements": 4, "duration_months": 35}, {"name": "Learning to Rank", "proficiency": "advanced", "endorsements": 12, "duration_months": 43}, {"name": "Illustrator", "proficiency": "beginner", "endorsements": 10, "duration_months": 12}, {"name": "GCP", "proficiency": "beginner", "endorsements": 4, "duration_months": 18}, {"name": "CNN", "proficiency": "intermediate", "endorsements": 7, "duration_months": 25}, {"name": "Computer Vision", "proficiency": "intermediate", "endorsements": 15, "duration_months": 23}, {"name": "QLoRA", "proficiency": "intermediate", "endorsements": 3, "duration_months": 10}, {"name": "Object Detection", "proficiency": "advanced", "endorsements": 10, "duration_months": 24}], "certifications": [], "languages": [{"language": "English", "proficiency": "native"}, {"language": "Hindi", "proficiency": "native"}], "redrob_signals": {"profile_completeness_score": 90.8, "signup_date": "2026-02-20", "last_active_date": "2026-04-03", "open_to_work_flag": true, "profile_views_received_30d": 182, "applications_submitted_30d": 3, "recruiter_response_rate": 0.84, "avg_response_time_hours": 28.0, "skill_assessment_scores": {"Qdrant": 63.7}, "connection_count": 145, "endorsements_received": 104, "notice_period_days": 60, "expected_salary_range_inr_lpa": {"min": 19.3, "max": 30.4}, "preferred_work_mode": "onsite", "willing_to_relocate": false, "github_activity_score": 69.5, "search_appearance_30d": 780, "saved_by_recruiters_30d": 31, "interview_completion_rate": 0.53, "offer_acceptance_rate": -1, "verified_email": true, "verified_phone": true, "linkedin_connected": false}}
{"candidate_id": "CAND_0037566", "profile": {"anonymized_name": "Ritu Nair", "headline": "Machine Learning Engineer | Search, Ranking & Retrieval", "summary": "Machine learning engineer with 6.9 years of experience building ML-powered features in production. Strong background in NLP, recommendation systems, and applied AI; comfortable across the ML stack from feature engineering through deployment. Recently, I shipped our first RAG-based feature this year and now own the eval framework for it. I've learned that most retrieval problems are actually evaluation problems in disguise. My academic background is in CS/ML but my main learning has come from shipping real systems and seeing what holds up under production load. Open to senior IC roles in applied ML or AI engineering, ideally at product companies where I'd own a meaningful piece of the ML stack.", "location": "Bangalore, Karnataka", "country": "India", "years_of_experience": 6.9, "current_title": "Machine Learning Engineer", "current_company": "LinkedIn", "current_company_size": "10001+", "current_industry": "Internet"}, "career_history": [{"company": "LinkedIn", "title": "Machine Learning Engineer", "start_date": "2022-03-19", "end_date": null, "duration_months": 51, "is_current": true, "industry": "Internet", "company_size": "10001+", "description": "Implemented a RAG-based customer support chatbot integrated with our existing ticketing system. Built the document ingestion pipeline (chunking, embedding via OpenAI embeddings, storing in Pinecone) and the answer-generation layer (initially GPT-4, then a fine-tuned smaller model for cost control). Designed the evaluation framework with both automatic metrics (BLEU, ROUGE) and human-in-the-loop quality scores. Deployment cut average ticket resolution time by 31% for the supported categories."}, {"company": "Paytm", "title": "Machine Learning Engineer", "start_date": "2019-08-02", "end_date": "2022-02-17", "duration_months": 31, "is_current": false, "industry": "Fintech", "company_size": "10001+", "description": "Implemented a RAG-based customer support chatbot integrated with our existing ticketing system. Built the document ingestion pipeline (chunking, embedding via OpenAI embeddings, storing in Pinecone) and the answer-generation layer (initially GPT-4, then a fine-tuned smaller model for cost control). Designed the evaluation framework with both automatic metrics (BLEU, ROUGE) and human-in-the-loop quality scores. Deployment cut average ticket resolution time by 31% for the supported categories."}], "education": [{"institution": "Jadavpur University", "degree": "B.Tech", "field_of_study": "Information Technology", "start_year": 2011, "end_year": 2014, "grade": "83%", "tier": "tier_2"}], "skills": [{"name": "Pinecone", "proficiency": "expert", "endorsements": 37, "duration_months": 74}, {"name": "Time Series", "proficiency": "advanced", "endorsements": 3, "duration_months": 49}, {"name": "LlamaIndex", "proficiency": "expert", "endorsements": 6, "duration_months": 59}, {"name": "NLP", "proficiency": "advanced", "endorsements": 58, "duration_months": 35}, {"name": "LoRA", "proficiency": "advanced", "endorsements": 7, "duration_months": 58}, {"name": "QLoRA", "proficiency": "expert", "endorsements": 28, "duration_months": 61}, {"name": "BM25", "proficiency": "advanced", "endorsements": 34, "duration_months": 19}, {"name": "Feature Engineering", "proficiency": "advanced", "endorsements": 45, "duration_months": 29}, {"name": "Forecasting", "proficiency": "advanced", "endorsements": 8, "duration_months": 47}, {"name": "MLflow", "proficiency": "advanced", "endorsements": 21, "duration_months": 42}, {"name": "Spring Boot", "proficiency": "beginner", "endorsements": 4, "duration_months": 12}, {"name": "TensorFlow", "proficiency": "expert", "endorsements": 16, "duration_months": 86}, {"name": "LLMs", "proficiency": "expert", "endorsements": 45, "duration_months": 40}, {"name": "YOLO", "proficiency": "advanced", "endorsements": 0, "duration_months": 58}, {"name": "LangChain", "proficiency": "expert", "endorsements": 36, "duration_months": 91}, {"name": "Elasticsearch", "proficiency": "expert", "endorsements": 60, "duration_months": 74}, {"name": "Reinforcement Learning", "proficiency": "intermediate", "endorsements": 10, "duration_months": 15}], "certifications": [], "languages": [{"language": "English", "proficiency": "native"}, {"language": "Hindi", "proficiency": "conversational"}], "redrob_signals": {"profile_completeness_score": 90.6, "signup_date": "2024-09-30", "last_active_date": "2026-04-20", "open_to_work_flag": true, "profile_views_received_30d": 110, "applications_submitted_30d": 2, "recruiter_response_rate": 0.5, "avg_response_time_hours": 70.4, "skill_assessment_scores": {"Pinecone": 82.4}, "connection_count": 778, "endorsements_received": 145, "notice_period_days": 15, "expected_salary_range_inr_lpa": {"min": 32.0, "max": 42.2}, "preferred_work_mode": "hybrid", "willing_to_relocate": true, "github_activity_score": 73.4, "search_appearance_30d": 708, "saved_by_recruiters_30d": 23, "interview_completion_rate": 0.84, "offer_acceptance_rate": 0.67, "verified_email": true, "verified_phone": true, "linkedin_connected": true}}
{"candidate_id": "CAND_0075249", "profile": {"anonymized_name": "Ishaan Arora", "headline": "Applied ML Engineer | Search, Ranking & Retrieval", "summary": "Machine learning engineer with 6.2 years of experience building ML-powered features in production. Strong background in NLP, recommendation systems, and applied AI; comfortable across the ML stack from feature engineering through deployment. Recently, I led the team that migrated our keyword-search-based product to embedding-based retrieval. I've spent enough time debugging production ranking issues to know which signals matter and which are noise. My academic background is in CS/ML but my main learning has come from shipping real systems and seeing what holds up under production load. Open to senior IC roles in applied ML or AI engineering, ideally at product companies where I'd own a meaningful piece of the ML stack.", "location": "Ahmedabad, Gujarat", "country": "India", "years_of_experience": 6.2, "current_title": "Applied ML Engineer", "current_company": "Zomato", "current_company_size": "5001-10000", "current_industry": "Food Delivery"}, "career_history": [{"company": "Zomato", "title": "Applied ML Engineer", "start_date": "2023-06-12", "end_date": null, "duration_months": 36, "is_current": true, "industry": "Food Delivery", "company_size": "5001-10000", "description": "Built a content recommendation system serving 10M+ users that combined collaborative filtering with content-based ranking. The system uses item-item similarity (via sentence-transformer embeddings) for cold starts and a gradient-boosted model trained on engagement signals for warm users. Most of my time went into the feature pipeline (~200 features) and the A/B testing infrastructure. The launch improved 7-day retention by 6% and time spent per session by 14%."}, {"company": "upGrad", "title": "Search Engineer", "start_date": "2020-04-21", "end_date": "2023-06-05", "duration_months": 38, "is_current": false, "industry": "EdTech", "company_size": "1001-5000", "description": "Developed a semantic search feature for an internal knowledge base of ~500K documents. Used sentence-transformers (all-MiniLM-L6-v2 initially, later upgraded to bge-base) with FAISS for fast nearest-neighbor retrieval. Designed the query expansion module that handles vocabulary mismatch between user queries and document terms. Reported search-relevance improvement of 35% over the prior Elasticsearch BM25 setup, validated through human relevance judgments."}], "education": [{"institution": "IIT Hyderabad", "degree": "M.Tech", "field_of_study": "Artificial Intelligence", "start_year": 2009, "end_year": 2013, "grade": "76%", "tier": "tier_1"}, {"institution": "NIT Warangal", "degree": "M.E.", "field_of_study": "Computer Engineering", "start_year": 2006, "end_year": 2011, "grade": "7.31 CGPA", "tier": "tier_1"}], "skills": [{"name": "Sentence Transformers", "proficiency": "expert", "endorsements": 12, "duration_months": 68}, {"name": "Milvus", "proficiency": "expert", "endorsements": 17, "duration_months": 77}, {"name": "Machine Learning", "proficiency": "expert", "endorsements": 46, "duration_months": 68}, {"name": "Fine-tuning LLMs", "proficiency": "expert", "endorsements": 11, "duration_months": 83}, {"name": "MLflow", "proficiency": "advanced", "endorsements": 10, "duration_months": 56}, {"name": "Hadoop", "proficiency": "beginner", "endorsements": 8, "duration_months": 14}, {"name": "BM25", "proficiency": "expert", "endorsements": 14, "duration_months": 40}, {"name": "Pinecone", "proficiency": "expert", "endorsements": 45, "duration_months": 80}, {"name": "Haystack", "proficiency": "expert", "endorsements": 52, "duration_months": 76}, {"name": "Time Series", "proficiency": "intermediate", "endorsements": 9, "duration_months": 17}, {"name": "Diffusion Models", "proficiency": "intermediate", "endorsements": 13, "duration_months": 21}, {"name": "ASR", "proficiency": "intermediate", "endorsements": 2, "duration_months": 21}, {"name": "Kubeflow", "proficiency": "advanced", "endorsements": 53, "duration_months": 56}, {"name": "Learning to Rank", "proficiency": "expert", "endorsements": 8, "duration_months": 37}], "certifications": [{"name": "NLP Specialization", "issuer": "Coursera/DeepLearning.AI", "year": 2019}], "languages": [{"language": "English", "proficiency": "professional"}, {"language": "Hindi", "proficiency": "native"}], "redrob_signals": {"profile_completeness_score": 70.1, "signup_date": "2024-07-31", "last_active_date": "2026-04-09", "open_to_work_flag": true, "profile_views_received_30d": 172, "applications_submitted_30d": 18, "recruiter_response_rate": 0.82, "avg_response_time_hours": 23.8, "skill_assessment_scores": {"Sentence Transformers": 80.7, "Milvus": 77.9, "Machine Learning": 69.0, "Fine-tuning LLMs": 57.6, "MLflow": 54.7}, "connection_count": 526, "endorsements_received": 143, "notice_period_days": 45, "expected_salary_range_inr_lpa": {"min": 27.5, "max": 60.3}, "preferred_work_mode": "hybrid", "willing_to_relocate": false, "github_activity_score": 36.8, "search_appearance_30d": 850, "saved_by_recruiters_30d": 9, "interview_completion_rate": 0.63, "offer_acceptance_rate": 0.43, "verified_email": false, "verified_phone": true, "linkedin_connected": true}}
{"candidate_id": "CAND_0010603", "profile": {"anonymized_name": "Aisha Desai", "headline": "ML Engineer | Building ML-powered solutions", "summary": "Data scientist / ML engineer with 5.3 years of experience in applied machine learning. Worked across predictive modeling, NLP, analytics, and lightweight deployment workflows. My current role is split between dashboarding/analytics and shipping production ML models. I'm strongest at the modeling and analysis side; comfortable with Python, scikit-learn, pandas, and standard MLOps tooling, but I'm still building depth on the engineering and infra side of production ML. I'm looking to grow into a deeper AI/ML system-building role \u2014 closer to retrieval, LLMs, and modern ranking systems.", "location": "Bangalore, Karnataka", "country": "India", "years_of_experience": 5.3, "current_title": "ML Engineer", "current_company": "BYJU'S", "current_company_size": "10001+", "current_industry": "EdTech"}, "career_history": [{"company": "BYJU'S", "title": "ML Engineer", "start_date": "2022-09-15", "end_date": null, "duration_months": 45, "is_current": true, "industry": "EdTech", "company_size": "10001+", "description": "Contributed to ML feature engineering and model deployment for a fraud-detection product. My main role was engineering: building the Flask-based prediction API, integrating with the feature store, and writing the model-serving observability layer. I worked closely with senior data scientists but my own modeling work was secondary \u2014 I was the production-side engineer."}, {"company": "BYJU'S", "title": "Data Scientist", "start_date": "2021-01-23", "end_date": "2022-07-17", "duration_months": 18, "is_current": false, "industry": "EdTech", "company_size": "10001+", "description": "Built recommendation-style features at a mid-stage startup \u2014 lighter weight than ranking systems at FAANG, but production. Used a combination of collaborative filtering (matrix factorization in implicit-feedback library) and gradient-boosted re-ranking over engagement signals. Pure ML side of the work; production deployment was handled by the platform team."}], "education": [{"institution": "Amity University", "degree": "M.E.", "field_of_study": "Electrical Engineering", "start_year": 2004, "end_year": 2007, "grade": "8.37 CGPA", "tier": "tier_3"}, {"institution": "Anna University", "degree": "B.Tech", "field_of_study": "Artificial Intelligence", "start_year": 2017, "end_year": 2021, "grade": "7.48 CGPA", "tier": "tier_2"}], "skills": [{"name": "Information Retrieval", "proficiency": "intermediate", "endorsements": 1, "duration_months": 26}, {"name": "TTS", "proficiency": "intermediate", "endorsements": 7, "duration_months": 36}, {"name": "OpenSearch", "proficiency": "advanced", "endorsements": 60, "duration_months": 25}, {"name": "Image Classification", "proficiency": "advanced", "endorsements": 22, "duration_months": 46}, {"name": "GANs", "proficiency": "intermediate", "endorsements": 15, "duration_months": 30}, {"name": "Docker", "proficiency": "intermediate", "endorsements": 7, "duration_months": 18}, {"name": "Azure", "proficiency": "beginner", "endorsements": 5, "duration_months": 2}, {"name": "pgvector", "proficiency": "intermediate", "endorsements": 5, "duration_months": 32}, {"name": "Deep Learning", "proficiency": "advanced", "endorsements": 45, "duration_months": 22}, {"name": "Machine Learning", "proficiency": "intermediate", "endorsements": 1, "duration_months": 34}, {"name": "LlamaIndex", "proficiency": "intermediate", "endorsements": 2, "duration_months": 16}, {"name": "Forecasting", "proficiency": "advanced", "endorsements": 56, "duration_months": 50}, {"name": "MLOps", "proficiency": "intermediate", "endorsements": 2, "duration_months": 19}, {"name": "Statistical Modeling", "proficiency": "intermediate", "endorsements": 0, "duration_months": 31}, {"name": "PEFT", "proficiency": "intermediate", "endorsements": 2, "duration_months": 23}], "certifications": [], "languages": [{"language": "English", "proficiency": "professional"}, {"language": "Hindi", "proficiency": "conversational"}], "redrob_signals": {"profile_completeness_score": 92.8, "signup_date": "2025-03-07", "last_active_date": "2026-05-23", "open_to_work_flag": true, "profile_views_received_30d": 80, "applications_submitted_30d": 2, "recruiter_response_rate": 0.94, "avg_response_time_hours": 4.4, "skill_assessment_scores": {}, "connection_count": 266, "endorsements_received": 53, "notice_period_days": 90, "expected_salary_range_inr_lpa": {"min": 18.0, "max": 32.6}, "preferred_work_mode": "flexible", "willing_to_relocate": false, "github_activity_score": 78.9, "search_appearance_30d": 700, "saved_by_recruiters_30d": 45, "interview_completion_rate": 0.64, "offer_acceptance_rate": 0.6, "verified_email": true, "verified_phone": true, "linkedin_connected": true}}
{"candidate_id": "CAND_0068932", "profile": {"anonymized_name": "Anil Mukherjee", "headline": "ML Engineer | Data Science & ML enthusiast", "summary": "Data scientist / ML engineer with 5.2 years of experience in applied machine learning. Worked across predictive modeling, NLP, analytics, and lightweight deployment workflows. I've spent the last couple of years building NLP-based classification and information extraction pipelines. I'm strongest at the modeling and analysis side; comfortable with Python, scikit-learn, pandas, and standard MLOps tooling, but I'm still building depth on the engineering and infra side of production ML. I'm looking to grow into a deeper AI/ML system-building role \u2014 closer to retrieval, LLMs, and modern ranking systems.", "location": "Noida, Uttar Pradesh", "country": "India", "years_of_experience": 5.2, "current_title": "ML Engineer", "current_company": "Krutrim", "current_company_size": "201-500", "current_industry": "AI/ML"}, "career_history": [{"company": "Krutrim", "title": "ML Engineer", "start_date": "2022-09-15", "end_date": null, "duration_months": 45, "is_current": true, "industry": "AI/ML", "company_size": "201-500", "description": "Built recommendation-style features at a mid-stage startup \u2014 lighter weight than ranking systems at FAANG, but production. Used a combination of collaborative filtering (matrix factorization in implicit-feedback library) and gradient-boosted re-ranking over engagement signals. Pure ML side of the work; production deployment was handled by the platform team."}, {"company": "Vedantu", "title": "Computer Vision Engineer", "start_date": "2021-05-23", "end_date": "2022-09-15", "duration_months": 16, "is_current": false, "industry": "EdTech", "company_size": "1001-5000", "description": "Worked on time-series forecasting models for supply-chain demand prediction at a logistics company. Built models in Prophet, LightGBM, and (for one project) a small LSTM \u2014 the LightGBM model ended up shipping. Also ran some reinforcement learning experiments for dynamic pricing but those didn't make it to production. The work was a mix of modeling, analysis, and stakeholder communication with the operations team."}], "education": [{"institution": "Amity University", "degree": "M.S.", "field_of_study": "Electrical Engineering", "start_year": 2010, "end_year": 2015, "grade": "75%", "tier": "tier_3"}], "skills": [{"name": "Marketing", "proficiency": "beginner", "endorsements": 11, "duration_months": 5}, {"name": "JavaScript", "proficiency": "beginner", "endorsements": 0, "duration_months": 12}, {"name": "Agile", "proficiency": "intermediate", "endorsements": 6, "duration_months": 10}, {"name": "MLOps", "proficiency": "advanced", "endorsements": 25, "duration_months": 29}, {"name": "Reinforcement Learning", "proficiency": "advanced", "endorsements": 54, "duration_months": 23}, {"name": "Milvus", "proficiency": "intermediate", "endorsements": 15, "duration_months": 16}, {"name": "Prompt Engineering", "proficiency": "intermediate", "endorsements": 7, "duration_months": 28}, {"name": "RAG", "proficiency": "advanced", "endorsements": 17, "duration_months": 28}, {"name": "scikit-learn", "proficiency": "intermediate", "endorsements": 12, "duration_months": 22}, {"name": "Diffusion Models", "proficiency": "advanced", "endorsements": 2, "duration_months": 52}, {"name": "CNN", "proficiency": "advanced", "endorsements": 34, "duration_months": 28}, {"name": "OpenSearch", "proficiency": "advanced", "endorsements": 40, "duration_months": 44}], "certifications": [{"name": "Deep Learning Specialization", "issuer": "Coursera/DeepLearning.AI", "year": 2024}, {"name": "AWS Certified Machine Learning Specialty", "issuer": "AWS", "year": 2024}], "languages": [{"language": "English", "proficiency": "professional"}, {"language": "Hindi", "proficiency": "professional"}], "redrob_signals": {"profile_completeness_score": 95.3, "signup_date": "2025-10-17", "last_active_date": "2026-05-26", "open_to_work_flag": true, "profile_views_received_30d": 188, "applications_submitted_30d": 10, "recruiter_response_rate": 0.82, "avg_response_time_hours": 3.9, "skill_assessment_scores": {"MLOps": 52.9, "Reinforcement Learning": 61.2}, "connection_count": 216, "endorsements_received": 26, "notice_period_days": 30, "expected_salary_range_inr_lpa": {"min": 16.9, "max": 31.5}, "preferred_work_mode": "hybrid", "willing_to_relocate": true, "github_activity_score": 70.5, "search_appearance_30d": 679, "saved_by_recruiters_30d": 26, "interview_completion_rate": 0.64, "offer_acceptance_rate": -1, "verified_email": true, "verified_phone": false, "linkedin_connected": true}}
{"candidate_id": "CAND_0040887", "profile": {"anonymized_name": "Meera Kumar", "headline": "Machine Learning Engineer | Search, Ranking & Retrieval", "summary": "Machine learning engineer with 4.7 years of experience building ML-powered features in production. Strong background in NLP, recommendation systems, and applied AI; comfortable across the ML stack from feature engineering through deployment. Recently, I've been the de-facto ML lead on a small team, shipping models across NLP and recsys. I've spent enough time debugging production ranking issues to know which signals matter and which are noise. My academic background is in CS/ML but my main learning has come from shipping real systems and seeing what holds up under production load. Open to senior IC roles in applied ML or AI engineering, ideally at product companies where I'd own a meaningful piece of the ML stack.", "location": "Toronto", "country": "Canada", "years_of_experience": 4.7, "current_title": "Machine Learning Engineer", "current_company": "Netflix", "current_company_size": "10001+", "current_industry": "Media"}, "career_history": [{"company": "Netflix", "title": "Machine Learning Engineer", "start_date": "2022-08-16", "end_date": null, "duration_months": 46, "is_current": true, "industry": "Media", "company_size": "10001+", "description": "Developed a semantic search feature for an internal knowledge base of ~500K documents. Used sentence-transformers (all-MiniLM-L6-v2 initially, later upgraded to bge-base) with FAISS for fast nearest-neighbor retrieval. Designed the query expansion module that handles vocabulary mismatch between user queries and document terms. Reported search-relevance improvement of 35% over the prior Elasticsearch BM25 setup, validated through human relevance judgments."}, {"company": "Unacademy", "title": "AI Engineer", "start_date": "2021-10-20", "end_date": "2022-07-17", "duration_months": 9, "is_current": false, "industry": "EdTech", "company_size": "5001-10000", "description": "Implemented a RAG-based customer support chatbot integrated with our existing ticketing system. Built the document ingestion pipeline (chunking, embedding via OpenAI embeddings, storing in Pinecone) and the answer-generation layer (initially GPT-4, then a fine-tuned smaller model for cost control). Designed the evaluation framework with both automatic metrics (BLEU, ROUGE) and human-in-the-loop quality scores. Deployment cut average ticket resolution time by 31% for the supported categories."}], "education": [{"institution": "SRM University", "degree": "B.Sc", "field_of_study": "Artificial Intelligence", "start_year": 2016, "end_year": 2021, "grade": "71%", "tier": "tier_2"}], "skills": [{"name": "Reinforcement Learning", "proficiency": "advanced", "endorsements": 2, "duration_months": 52}, {"name": "Computer Vision", "proficiency": "advanced", "endorsements": 6, "duration_months": 53}, {"name": "SEO", "proficiency": "beginner", "endorsements": 1, "duration_months": 3}, {"name": "FAISS", "proficiency": "advanced", "endorsements": 17, "duration_months": 52}, {"name": "MLflow", "proficiency": "intermediate", "endorsements": 15, "duration_months": 34}, {"name": "LoRA", "proficiency": "expert", "endorsements": 4, "duration_months": 85}, {"name": "LangChain", "proficiency": "expert", "endorsements": 43, "duration_months": 39}, {"name": "Python", "proficiency": "expert", "endorsements": 40, "duration_months": 84}, {"name": "PEFT", "proficiency": "expert", "endorsements": 48, "duration_months": 74}, {"name": "Milvus", "proficiency": "expert", "endorsements": 1, "duration_months": 81}, {"name": "Deep Learning", "proficiency": "advanced", "endorsements": 21, "duration_months": 46}, {"name": "Recommendation Systems", "proficiency": "expert", "endorsements": 47, "duration_months": 71}], "certifications": [{"name": "LangChain for LLM Application Development", "issuer": "DeepLearning.AI", "year": 2019}, {"name": "Google Cloud Professional ML Engineer", "issuer": "Google Cloud", "year": 2022}], "languages": [{"language": "English", "proficiency": "professional"}, {"language": "Hindi", "proficiency": "native"}], "redrob_signals": {"profile_completeness_score": 93.1, "signup_date": "2024-11-20", "last_active_date": "2026-05-24", "open_to_work_flag": true, "profile_views_received_30d": 114, "applications_submitted_30d": 9, "recruiter_response_rate": 0.84, "avg_response_time_hours": 60.7, "skill_assessment_scores": {"Reinforcement Learning": 85.8, "Computer Vision": 55.4, "FAISS": 70.8}, "connection_count": 1053, "endorsements_received": 162, "notice_period_days": 15, "expected_salary_range_inr_lpa": {"min": 23.8, "max": 52.6}, "preferred_work_mode": "remote", "willing_to_relocate": false, "github_activity_score": 34.2, "search_appearance_30d": 846, "saved_by_recruiters_30d": 50, "interview_completion_rate": 0.8, "offer_acceptance_rate": 0.88, "verified_email": true, "verified_phone": true, "linkedin_connected": true}}
{"candidate_id": "CAND_0010685", "profile": {"anonymized_name": "Sunil Mishra", "headline": "NLP Engineer | Applied ML | Building intelligent products", "summary": "Machine learning engineer with 6.7 years of experience building ML-powered features in production. Strong background in NLP, recommendation systems, and applied AI; comfortable across the ML stack from feature engineering through deployment. Recently, I built our semantic search infrastructure from scratch \u2014 sentence-transformers, FAISS, the works. I've spent enough time debugging production ranking issues to know which signals matter and which are noise. My academic background is in CS/ML but my main learning has come from shipping real systems and seeing what holds up under production load. Open to senior IC roles in applied ML or AI engineering, ideally at product companies where I'd own a meaningful piece of the ML stack.", "location": "Kolkata, West Bengal", "country": "India", "years_of_experience": 6.7, "current_title": "NLP Engineer", "current_company": "Rephrase.ai", "current_company_size": "11-50", "current_industry": "AI/ML"}, "career_history": [{"company": "Rephrase.ai", "title": "NLP Engineer", "start_date": "2024-12-03", "end_date": null, "duration_months": 18, "is_current": true, "industry": "AI/ML", "company_size": "11-50", "description": "Owned the ranking layer for an e-commerce search product, evolving it from a hand-tuned scoring function to a learning-to-rank model over 9 months. Designed the relevance labeling pipeline (mix of click-through data and explicit human judgments), the feature pipeline, and the training/eval workflow. Most of the work was infrastructure and data quality \u2014 the modeling part was almost the easy bit. Final model improved revenue-per-search by 12%."}, {"company": "Microsoft", "title": "NLP Engineer", "start_date": "2022-08-16", "end_date": "2024-12-03", "duration_months": 28, "is_current": false, "industry": "Software", "company_size": "10001+", "description": "Built and operated production ML pipelines using MLflow for experiment tracking, Kubeflow for orchestration, and our internal feature store. My main project was a churn prediction model that's now used by the customer success team to prioritize outreach. Designed the model monitoring stack: data drift detection, prediction distribution checks, and alerting. Mentored a junior engineer through their first end-to-end ML project last year."}, {"company": "Mad Street Den", "title": "Machine Learning Engineer", "start_date": "2021-07-22", "end_date": "2022-07-17", "duration_months": 12, "is_current": false, "industry": "AI/ML", "company_size": "201-500", "description": "Developed a semantic search feature for an internal knowledge base of ~500K documents. Used sentence-transformers (all-MiniLM-L6-v2 initially, later upgraded to bge-base) with FAISS for fast nearest-neighbor retrieval. Designed the query expansion module that handles vocabulary mismatch between user queries and document terms. Reported search-relevance improvement of 35% over the prior Elasticsearch BM25 setup, validated through human relevance judgments."}, {"company": "Mad Street Den", "title": "Search Engineer", "start_date": "2019-10-24", "end_date": "2021-07-15", "duration_months": 21, "is_current": false, "industry": "AI/ML", "company_size": "201-500", "description": "Trained and shipped multiple ranking models for our product's discovery feed using XGBoost and LightGBM. Designed features across three families: content metadata, user behavior signals, and item engagement history. Owned the offline-online correlation analysis that determined which offline metrics actually predicted A/B test outcomes. Worked closely with PMs to define the optimization target (click-through vs. dwell time vs. downstream conversion) \u2014 that work was as important as the modeling itself."}], "education": [{"institution": "Christ University", "degree": "B.Tech", "field_of_study": "Data Science", "start_year": 2004, "end_year": 2008, "grade": "8.80 CGPA", "tier": "tier_3"}], "skills": [{"name": "Prompt Engineering", "proficiency": "expert", "endorsements": 50, "duration_months": 56}, {"name": "LlamaIndex", "proficiency": "expert", "endorsements": 47, "duration_months": 71}, {"name": "Deep Learning", "proficiency": "advanced", "endorsements": 56, "duration_months": 36}, {"name": "Statistical Modeling", "proficiency": "intermediate", "endorsements": 9, "duration_months": 23}, {"name": "PyTorch", "proficiency": "expert", "endorsements": 35, "duration_months": 83}, {"name": "Information Retrieval", "proficiency": "expert", "endorsements": 43, "duration_months": 41}, {"name": "ASR", "proficiency": "intermediate", "endorsements": 1, "duration_months": 36}, {"name": "Elasticsearch", "proficiency": "expert", "endorsements": 51, "duration_months": 78}, {"name": "MLflow", "proficiency": "advanced", "endorsements": 23, "duration_months": 43}, {"name": "Java", "proficiency": "intermediate", "endorsements": 13, "duration_months": 18}, {"name": "Weights & Biases", "proficiency": "advanced", "endorsements": 60, "duration_months": 55}, {"name": "LangChain", "proficiency": "advanced", "endorsements": 15, "duration_months": 26}, {"name": "Diffusion Models", "proficiency": "advanced", "endorsements": 34, "duration_months": 31}, {"name": "Haystack", "proficiency": "advanced", "endorsements": 57, "duration_months": 26}, {"name": "RAG", "proficiency": "expert", "endorsements": 53, "duration_months": 59}, {"name": "Photoshop", "proficiency": "intermediate", "endorsements": 11, "duration_months": 19}], "certifications": [{"name": "Google Cloud Professional ML Engineer", "issuer": "Google Cloud", "year": 2023}], "languages": [{"language": "English", "proficiency": "professional"}, {"language": "Hindi", "proficiency": "conversational"}], "redrob_signals": {"profile_completeness_score": 70.7, "signup_date": "2024-06-15", "last_active_date": "2026-03-16", "open_to_work_flag": true, "profile_views_received_30d": 246, "applications_submitted_30d": 13, "recruiter_response_rate": 0.83, "avg_response_time_hours": 77.7, "skill_assessment_scores": {}, "connection_count": 1202, "endorsements_received": 123, "notice_period_days": 30, "expected_salary_range_inr_lpa": {"min": 22.6, "max": 64.5}, "preferred_work_mode": "remote", "willing_to_relocate": false, "github_activity_score": -1, "search_appearance_30d": 779, "saved_by_recruiters_30d": 46, "interview_completion_rate": 0.89, "offer_acceptance_rate": -1, "verified_email": true, "verified_phone": true, "linkedin_connected": false}}
{"candidate_id": "CAND_0027691", "profile": {"anonymized_name": "Ayaan Goyal", "headline": "NLP Engineer | ML, NLP, Recommendation Systems", "summary": "Machine learning engineer with 6.5 years of experience building ML-powered features in production. Strong background in NLP, recommendation systems, and applied AI; comfortable across the ML stack from feature engineering through deployment. Recently, my main project for the last 18 months has been the recommendation system that powers our discovery feed. Along the way I've gotten comfortable with the operational side \u2014 A/B testing, drift monitoring, retraining schedules. My academic background is in CS/ML but my main learning has come from shipping real systems and seeing what holds up under production load. Open to senior IC roles in applied ML or AI engineering, ideally at product companies where I'd own a meaningful piece of the ML stack.", "location": "Pune, Maharashtra", "country": "India", "years_of_experience": 6.5, "current_title": "NLP Engineer", "current_company": "Haptik", "current_company_size": "201-500", "current_industry": "Conversational AI"}, "career_history": [{"company": "Haptik", "title": "NLP Engineer", "start_date": "2024-03-08", "end_date": null, "duration_months": 27, "is_current": true, "industry": "Conversational AI", "company_size": "201-500", "description": "Built and operated production ML pipelines using MLflow for experiment tracking, Kubeflow for orchestration, and our internal feature store. My main project was a churn prediction model that's now used by the customer success team to prioritize outreach. Designed the model monitoring stack: data drift detection, prediction distribution checks, and alerting. Mentored a junior engineer through their first end-to-end ML project last year."}, {"company": "Vedantu", "title": "Applied ML Engineer", "start_date": "2021-06-15", "end_date": "2024-03-01", "duration_months": 33, "is_current": false, "industry": "EdTech", "company_size": "1001-5000", "description": "Developed a semantic search feature for an internal knowledge base of ~500K documents. Used sentence-transformers (all-MiniLM-L6-v2 initially, later upgraded to bge-base) with FAISS for fast nearest-neighbor retrieval. Designed the query expansion module that handles vocabulary mismatch between user queries and document terms. Reported search-relevance improvement of 35% over the prior Elasticsearch BM25 setup, validated through human relevance judgments."}, {"company": "Meta", "title": "AI Engineer", "start_date": "2020-02-21", "end_date": "2021-06-15", "duration_months": 16, "is_current": false, "industry": "Internet", "company_size": "10001+", "description": "Owned the ranking layer for an e-commerce search product, evolving it from a hand-tuned scoring function to a learning-to-rank model over 9 months. Designed the relevance labeling pipeline (mix of click-through data and explicit human judgments), the feature pipeline, and the training/eval workflow. Most of the work was infrastructure and data quality \u2014 the modeling part was almost the easy bit. Final model improved revenue-per-search by 12%."}], "education": [{"institution": "Thapar University", "degree": "M.Sc", "field_of_study": "Machine Learning", "start_year": 2007, "end_year": 2010, "grade": "6.85 CGPA", "tier": "tier_2"}, {"institution": "KIIT University", "degree": "M.E.", "field_of_study": "Data Science", "start_year": 2008, "end_year": 2012, "grade": "73%", "tier": "tier_3"}], "skills": [{"name": "SAP", "proficiency": "beginner", "endorsements": 0, "duration_months": 8}, {"name": "LoRA", "proficiency": "expert", "endorsements": 21, "duration_months": 72}, {"name": "PEFT", "proficiency": "advanced", "endorsements": 28, "duration_months": 38}, {"name": "Recommendation Systems", "proficiency": "expert", "endorsements": 55, "duration_months": 92}, {"name": "Statistical Modeling", "proficiency": "intermediate", "endorsements": 9, "duration_months": 29}, {"name": "Weaviate", "proficiency": "expert", "endorsements": 38, "duration_months": 73}, {"name": "scikit-learn", "proficiency": "advanced", "endorsements": 40, "duration_months": 49}, {"name": "Marketing", "proficiency": "beginner", "endorsements": 12, "duration_months": 10}, {"name": "Embeddings", "proficiency": "advanced", "endorsements": 14, "duration_months": 18}, {"name": "Learning to Rank", "proficiency": "advanced", "endorsements": 18, "duration_months": 39}, {"name": "TTS", "proficiency": "intermediate", "endorsements": 15, "duration_months": 19}, {"name": "QLoRA", "proficiency": "expert", "endorsements": 47, "duration_months": 46}, {"name": "TensorFlow", "proficiency": "expert", "endorsements": 2, "duration_months": 86}, {"name": "Semantic Search", "proficiency": "expert", "endorsements": 13, "duration_months": 53}, {"name": "Python", "proficiency": "advanced", "endorsements": 23, "duration_months": 27}, {"name": "MLflow", "proficiency": "advanced", "endorsements": 2, "duration_months": 60}, {"name": "Computer Vision", "proficiency": "advanced", "endorsements": 32, "duration_months": 42}], "certifications": [], "languages": [{"language": "English", "proficiency": "native"}, {"language": "Hindi", "proficiency": "native"}], "redrob_signals": {"profile_completeness_score": 96.3, "signup_date": "2024-09-14", "last_active_date": "2026-03-31", "open_to_work_flag": true, "profile_views_received_30d": 14, "applications_submitted_30d": 1, "recruiter_response_rate": 0.68, "avg_response_time_hours": 14.7, "skill_assessment_scores": {"LoRA": 86.9, "PEFT": 53.6, "Recommendation Systems": 63.9, "Weaviate": 79.3, "scikit-learn": 64.2}, "connection_count": 447, "endorsements_received": 154, "notice_period_days": 15, "expected_salary_range_inr_lpa": {"min": 26.1, "max": 65.0}, "preferred_work_mode": "hybrid", "willing_to_relocate": false, "github_activity_score": 58.5, "search_appearance_30d": 100, "saved_by_recruiters_30d": 25, "interview_completion_rate": 0.63, "offer_acceptance_rate": 0.62, "verified_email": true, "verified_phone": true, "linkedin_connected": true}}
{"candidate_id": "CAND_0069905", "profile": {"anonymized_name": "Nisha Bansal", "headline": "Applied ML Engineer | Search, Ranking & Retrieval", "summary": "Machine learning engineer with 6.6 years of experience building ML-powered features in production. Strong background in NLP, recommendation systems, and applied AI; comfortable across the ML stack from feature engineering through deployment. Recently, I led the team that migrated our keyword-search-based product to embedding-based retrieval. Along the way I've gotten comfortable with the operational side \u2014 A/B testing, drift monitoring, retraining schedules. My academic background is in CS/ML but my main learning has come from shipping real systems and seeing what holds up under production load. Open to senior IC roles in applied ML or AI engineering, ideally at product companies where I'd own a meaningful piece of the ML stack.", "location": "Bhubaneswar, Odisha", "country": "India", "years_of_experience": 6.6, "current_title": "Applied ML Engineer", "current_company": "Sarvam AI", "current_company_size": "51-200", "current_industry": "AI/ML"}, "career_history": [{"company": "Sarvam AI", "title": "Applied ML Engineer", "start_date": "2024-04-07", "end_date": null, "duration_months": 26, "is_current": true, "industry": "AI/ML", "company_size": "51-200", "description": "Developed a semantic search feature for an internal knowledge base of ~500K documents. Used sentence-transformers (all-MiniLM-L6-v2 initially, later upgraded to bge-base) with FAISS for fast nearest-neighbor retrieval. Designed the query expansion module that handles vocabulary mismatch between user queries and document terms. Reported search-relevance improvement of 35% over the prior Elasticsearch BM25 setup, validated through human relevance judgments."}, {"company": "Nykaa", "title": "Machine Learning Engineer", "start_date": "2023-02-12", "end_date": "2024-03-08", "duration_months": 13, "is_current": false, "industry": "E-commerce", "company_size": "1001-5000", "description": "Implemented a RAG-based customer support chatbot integrated with our existing ticketing system. Built the document ingestion pipeline (chunking, embedding via OpenAI embeddings, storing in Pinecone) and the answer-generation layer (initially GPT-4, then a fine-tuned smaller model for cost control). Designed the evaluation framework with both automatic metrics (BLEU, ROUGE) and human-in-the-loop quality scores. Deployment cut average ticket resolution time by 31% for the supported categories."}, {"company": "Observe.AI", "title": "Recommendation Systems Engineer", "start_date": "2019-11-30", "end_date": "2023-02-12", "duration_months": 39, "is_current": false, "industry": "AI/ML", "company_size": "201-500", "description": "Developed a semantic search feature for an internal knowledge base of ~500K documents. Used sentence-transformers (all-MiniLM-L6-v2 initially, later upgraded to bge-base) with FAISS for fast nearest-neighbor retrieval. Designed the query expansion module that handles vocabulary mismatch between user queries and document terms. Reported search-relevance improvement of 35% over the prior Elasticsearch BM25 setup, validated through human relevance judgments."}], "education": [{"institution": "PES University", "degree": "M.E.", "field_of_study": "Computer Engineering", "start_year": 2003, "end_year": 2006, "grade": "7.67 CGPA", "tier": "tier_2"}], "skills": [{"name": "Flask", "proficiency": "beginner", "endorsements": 6, "duration_months": 2}, {"name": "Redux", "proficiency": "intermediate", "endorsements": 13, "duration_months": 16}, {"name": "LoRA", "proficiency": "expert", "endorsements": 46, "duration_months": 51}, {"name": "Computer Vision", "proficiency": "intermediate", "endorsements": 4, "duration_months": 13}, {"name": "Speech Recognition", "proficiency": "advanced", "endorsements": 11, "duration_months": 28}, {"name": "Image Classification", "proficiency": "advanced", "endorsements": 10, "duration_months": 20}, {"name": "Recommendation Systems", "proficiency": "expert", "endorsements": 13, "duration_months": 75}, {"name": "Sentence Transformers", "proficiency": "expert", "endorsements": 48, "duration_months": 55}, {"name": "LangChain", "proficiency": "expert", "endorsements": 35, "duration_months": 56}, {"name": "TensorFlow", "proficiency": "advanced", "endorsements": 47, "duration_months": 48}, {"name": "Hugging Face Transformers", "proficiency": "advanced", "endorsements": 21, "duration_months": 31}, {"name": "MLOps", "proficiency": "advanced", "endorsements": 35, "duration_months": 59}, {"name": "Forecasting", "proficiency": "intermediate", "endorsements": 1, "duration_months": 10}, {"name": "Python", "proficiency": "expert", "endorsements": 55, "duration_months": 42}, {"name": "Object Detection", "proficiency": "intermediate", "endorsements": 12, "duration_months": 18}, {"name": "Weaviate", "proficiency": "expert", "endorsements": 20, "duration_months": 78}, {"name": "Learning to Rank", "proficiency": "expert", "endorsements": 31, "duration_months": 41}], "certifications": [{"name": "AWS Certified Machine Learning Specialty", "issuer": "AWS", "year": 2020}], "languages": [{"language": "English", "proficiency": "native"}, {"language": "Hindi", "proficiency": "native"}], "redrob_signals": {"profile_completeness_score": 63.6, "signup_date": "2025-11-19", "last_active_date": "2026-04-21", "open_to_work_flag": true, "profile_views_received_30d": 59, "applications_submitted_30d": 18, "recruiter_response_rate": 0.78, "avg_response_time_hours": 68.8, "skill_assessment_scores": {"LoRA": 71.3}, "connection_count": 883, "endorsements_received": 108, "notice_period_days": 90, "expected_salary_range_inr_lpa": {"min": 24.8, "max": 58.9}, "preferred_work_mode": "onsite", "willing_to_relocate": true, "github_activity_score": 44.8, "search_appearance_30d": 315, "saved_by_recruiters_30d": 57, "interview_completion_rate": 0.93, "offer_acceptance_rate": 0.53, "verified_email": true, "verified_phone": true, "linkedin_connected": true}}
{"candidate_id": "CAND_0091909", "profile": {"anonymized_name": "Pooja Tiwari", "headline": "Machine Learning Engineer | ML, NLP, Recommendation Systems", "summary": "Machine learning engineer with 6.9 years of experience building ML-powered features in production. Strong background in NLP, recommendation systems, and applied AI; comfortable across the ML stack from feature engineering through deployment. Recently, I've been the de-facto ML lead on a small team, shipping models across NLP and recsys. I've learned that most retrieval problems are actually evaluation problems in disguise. My academic background is in CS/ML but my main learning has come from shipping real systems and seeing what holds up under production load. Open to senior IC roles in applied ML or AI engineering, ideally at product companies where I'd own a meaningful piece of the ML stack.", "location": "Bangalore, Karnataka", "country": "India", "years_of_experience": 6.9, "current_title": "Machine Learning Engineer", "current_company": "Rephrase.ai", "current_company_size": "11-50", "current_industry": "AI/ML"}, "career_history": [{"company": "Rephrase.ai", "title": "Machine Learning Engineer", "start_date": "2022-03-19", "end_date": null, "duration_months": 51, "is_current": true, "industry": "AI/ML", "company_size": "11-50", "description": "Built a content recommendation system serving 10M+ users that combined collaborative filtering with content-based ranking. The system uses item-item similarity (via sentence-transformer embeddings) for cold starts and a gradient-boosted model trained on engagement signals for warm users. Most of my time went into the feature pipeline (~200 features) and the A/B testing infrastructure. The launch improved 7-day retention by 6% and time spent per session by 14%."}, {"company": "Meta", "title": "NLP Engineer", "start_date": "2019-08-25", "end_date": "2022-03-12", "duration_months": 31, "is_current": false, "industry": "Internet", "company_size": "10001+", "description": "Trained and shipped multiple ranking models for our product's discovery feed using XGBoost and LightGBM. Designed features across three families: content metadata, user behavior signals, and item engagement history. Owned the offline-online correlation analysis that determined which offline metrics actually predicted A/B test outcomes. Worked closely with PMs to define the optimization target (click-through vs. dwell time vs. downstream conversion) \u2014 that work was as important as the modeling itself."}], "education": [{"institution": "IIIT Bangalore", "degree": "M.E.", "field_of_study": "Information Technology", "start_year": 2013, "end_year": 2016, "grade": "9.24 CGPA", "tier": "tier_1"}], "skills": [{"name": "LLMs", "proficiency": "expert", "endorsements": 16, "duration_months": 69}, {"name": "Image Classification", "proficiency": "advanced", "endorsements": 9, "duration_months": 19}, {"name": "Pinecone", "proficiency": "expert", "endorsements": 27, "duration_months": 84}, {"name": "Diffusion Models", "proficiency": "intermediate", "endorsements": 15, "duration_months": 30}, {"name": "QLoRA", "proficiency": "expert", "endorsements": 5, "duration_months": 89}, {"name": "Qdrant", "proficiency": "advanced", "endorsements": 55, "duration_months": 21}, {"name": "OpenSearch", "proficiency": "expert", "endorsements": 43, "duration_months": 59}, {"name": "BentoML", "proficiency": "intermediate", "endorsements": 13, "duration_months": 21}, {"name": "scikit-learn", "proficiency": "expert", "endorsements": 7, "duration_months": 91}, {"name": "Sentence Transformers", "proficiency": "expert", "endorsements": 30, "duration_months": 57}, {"name": "Hugging Face Transformers", "proficiency": "expert", "endorsements": 3, "duration_months": 94}, {"name": "Learning to Rank", "proficiency": "advanced", "endorsements": 40, "duration_months": 56}, {"name": "MLflow", "proficiency": "intermediate", "endorsements": 1, "duration_months": 34}, {"name": "LangChain", "proficiency": "expert", "endorsements": 16, "duration_months": 92}], "certifications": [{"name": "AWS Certified Machine Learning Specialty", "issuer": "AWS", "year": 2019}], "languages": [{"language": "English", "proficiency": "native"}, {"language": "Hindi", "proficiency": "professional"}], "redrob_signals": {"profile_completeness_score": 71.5, "signup_date": "2026-03-06", "last_active_date": "2026-03-18", "open_to_work_flag": true, "profile_views_received_30d": 42, "applications_submitted_30d": 0, "recruiter_response_rate": 0.65, "avg_response_time_hours": 27.7, "skill_assessment_scores": {"LLMs": 84.9, "Image Classification": 89.5}, "connection_count": 524, "endorsements_received": 32, "notice_period_days": 45, "expected_salary_range_inr_lpa": {"min": 29.7, "max": 61.1}, "preferred_work_mode": "hybrid", "willing_to_relocate": true, "github_activity_score": 49.7, "search_appearance_30d": 495, "saved_by_recruiters_30d": 64, "interview_completion_rate": 0.66, "offer_acceptance_rate": 0.34, "verified_email": true, "verified_phone": true, "linkedin_connected": false}}
{"candidate_id": "CAND_0067535", "profile": {"anonymized_name": "Aarohi Mukherjee", "headline": "Junior ML Engineer | Data Science & ML enthusiast", "summary": "Data scientist / ML engineer with 6.8 years of experience in applied machine learning. Worked across predictive modeling, NLP, analytics, and lightweight deployment workflows. I've spent the last couple of years building NLP-based classification and information extraction pipelines. I'm strongest at the modeling and analysis side; comfortable with Python, scikit-learn, pandas, and standard MLOps tooling, but I'm still building depth on the engineering and infra side of production ML. Looking for a role where I can step up to more end-to-end ownership of ML systems, not just modeling.", "location": "Jaipur, Rajasthan", "country": "India", "years_of_experience": 6.8, "current_title": "Junior ML Engineer", "current_company": "Locobuzz", "current_company_size": "51-200", "current_industry": "AI/ML"}, "career_history": [{"company": "Locobuzz", "title": "Junior ML Engineer", "start_date": "2024-04-07", "end_date": null, "duration_months": 26, "is_current": true, "industry": "AI/ML", "company_size": "51-200", "description": "Built computer vision models for our product's image moderation feature using PyTorch \u2014 fine-tuned ResNet variants on a labeled dataset of ~200K images. Set up the training pipeline (data loading, augmentation, evaluation) and the inference service. Most of my project work has been in CV; I'm now interested in transitioning toward NLP/LLM work but my professional experience there is limited."}, {"company": "HCL", "title": "Data Scientist", "start_date": "2022-10-15", "end_date": "2024-04-07", "duration_months": 18, "is_current": false, "industry": "IT Services", "company_size": "10001+", "description": "Contributed to ML feature engineering and model deployment for a fraud-detection product. My main role was engineering: building the Flask-based prediction API, integrating with the feature store, and writing the model-serving observability layer. I worked closely with senior data scientists but my own modeling work was secondary \u2014 I was the production-side engineer."}, {"company": "Flipkart", "title": "ML Engineer", "start_date": "2019-09-01", "end_date": "2022-09-15", "duration_months": 37, "is_current": false, "industry": "E-commerce", "company_size": "10001+", "description": "Built recommendation-style features at a mid-stage startup \u2014 lighter weight than ranking systems at FAANG, but production. Used a combination of collaborative filtering (matrix factorization in implicit-feedback library) and gradient-boosted re-ranking over engagement signals. Pure ML side of the work; production deployment was handled by the platform team."}], "education": [{"institution": "KIIT University", "degree": "B.E.", "field_of_study": "Information Technology", "start_year": 2001, "end_year": 2006, "grade": "82%", "tier": "tier_3"}], "skills": [{"name": "Deep Learning", "proficiency": "intermediate", "endorsements": 9, "duration_months": 27}, {"name": "BentoML", "proficiency": "advanced", "endorsements": 42, "duration_months": 21}, {"name": "Flask", "proficiency": "intermediate", "endorsements": 3, "duration_months": 16}, {"name": "Java", "proficiency": "beginner", "endorsements": 12, "duration_months": 14}, {"name": "YOLO", "proficiency": "advanced", "endorsements": 10, "duration_months": 22}, {"name": "Reinforcement Learning", "proficiency": "advanced", "endorsements": 13, "duration_months": 40}, {"name": "LangChain", "proficiency": "intermediate", "endorsements": 13, "duration_months": 15}, {"name": "Databricks", "proficiency": "beginner", "endorsements": 13, "duration_months": 11}, {"name": "scikit-learn", "proficiency": "intermediate", "endorsements": 3, "duration_months": 18}, {"name": "Prompt Engineering", "proficiency": "intermediate", "endorsements": 8, "duration_months": 10}, {"name": "Speech Recognition", "proficiency": "advanced", "endorsements": 12, "duration_months": 52}, {"name": "Excel", "proficiency": "intermediate", "endorsements": 14, "duration_months": 30}], "certifications": [], "languages": [{"language": "English", "proficiency": "professional"}, {"language": "Hindi", "proficiency": "native"}], "redrob_signals": {"profile_completeness_score": 95.6, "signup_date": "2024-02-22", "last_active_date": "2026-05-23", "open_to_work_flag": true, "profile_views_received_30d": 68, "applications_submitted_30d": 7, "recruiter_response_rate": 0.81, "avg_response_time_hours": 11.7, "skill_assessment_scores": {"BentoML": 72.3}, "connection_count": 1129, "endorsements_received": 74, "notice_period_days": 45, "expected_salary_range_inr_lpa": {"min": 28.3, "max": 46.3}, "preferred_work_mode": "flexible", "willing_to_relocate": false, "github_activity_score": 25.2, "search_appearance_30d": 629, "saved_by_recruiters_30d": 27, "interview_completion_rate": 0.87, "offer_acceptance_rate": -1, "verified_email": true, "verified_phone": true, "linkedin_connected": true}}
{"candidate_id": "CAND_0053591", "profile": {"anonymized_name": "Anjali Bhatia", "headline": "AI Engineer | Applied ML | Building intelligent products", "summary": "Machine learning engineer with 5.3 years of experience building ML-powered features in production. Strong background in NLP, recommendation systems, and applied AI; comfortable across the ML stack from feature engineering through deployment. Recently, I built our semantic search infrastructure from scratch \u2014 sentence-transformers, FAISS, the works. Along the way I've gotten comfortable with the operational side \u2014 A/B testing, drift monitoring, retraining schedules. My academic background is in CS/ML but my main learning has come from shipping real systems and seeing what holds up under production load. Open to senior IC roles in applied ML or AI engineering, ideally at product companies where I'd own a meaningful piece of the ML stack.", "location": "Toronto", "country": "Canada", "years_of_experience": 5.3, "current_title": "AI Engineer", "current_company": "Ola", "current_company_size": "5001-10000", "current_industry": "Transportation"}, "career_history": [{"company": "Ola", "title": "AI Engineer", "start_date": "2023-04-13", "end_date": null, "duration_months": 38, "is_current": true, "industry": "Transportation", "company_size": "5001-10000", "description": "Trained and shipped multiple ranking models for our product's discovery feed using XGBoost and LightGBM. Designed features across three families: content metadata, user behavior signals, and item engagement history. Owned the offline-online correlation analysis that determined which offline metrics actually predicted A/B test outcomes. Worked closely with PMs to define the optimization target (click-through vs. dwell time vs. downstream conversion) \u2014 that work was as important as the modeling itself."}, {"company": "Swiggy", "title": "AI Engineer", "start_date": "2021-03-24", "end_date": "2023-04-13", "duration_months": 25, "is_current": false, "industry": "Food Delivery", "company_size": "5001-10000", "description": "Implemented a RAG-based customer support chatbot integrated with our existing ticketing system. Built the document ingestion pipeline (chunking, embedding via OpenAI embeddings, storing in Pinecone) and the answer-generation layer (initially GPT-4, then a fine-tuned smaller model for cost control). Designed the evaluation framework with both automatic metrics (BLEU, ROUGE) and human-in-the-loop quality scores. Deployment cut average ticket resolution time by 31% for the supported categories."}], "education": [{"institution": "Anna University", "degree": "M.Tech", "field_of_study": "Information Technology", "start_year": 2009, "end_year": 2013, "grade": "66%", "tier": "tier_2"}], "skills": [{"name": "LangChain", "proficiency": "advanced", "endorsements": 34, "duration_months": 48}, {"name": "Reinforcement Learning", "proficiency": "advanced", "endorsements": 28, "duration_months": 20}, {"name": "Embeddings", "proficiency": "advanced", "endorsements": 50, "duration_months": 21}, {"name": "Milvus", "proficiency": "advanced", "endorsements": 15, "duration_months": 51}, {"name": "OpenSearch", "proficiency": "expert", "endorsements": 28, "duration_months": 58}, {"name": "Redux", "proficiency": "intermediate", "endorsements": 13, "duration_months": 13}, {"name": "Object Detection", "proficiency": "intermediate", "endorsements": 15, "duration_months": 29}, {"name": "Sentence Transformers", "proficiency": "expert", "endorsements": 59, "duration_months": 63}, {"name": "Data Pipelines", "proficiency": "beginner", "endorsements": 7, "duration_months": 3}, {"name": "Qdrant", "proficiency": "expert", "endorsements": 20, "duration_months": 41}, {"name": "Statistical Modeling", "proficiency": "advanced", "endorsements": 50, "duration_months": 23}, {"name": "BM25", "proficiency": "advanced", "endorsements": 36, "duration_months": 40}, {"name": "Six Sigma", "proficiency": "intermediate", "endorsements": 9, "duration_months": 25}, {"name": "RAG", "proficiency": "advanced", "endorsements": 13, "duration_months": 51}], "certifications": [{"name": "Google Cloud Professional ML Engineer", "issuer": "Google Cloud", "year": 2018}, {"name": "AWS Certified Machine Learning Specialty", "issuer": "AWS", "year": 2021}], "languages": [{"language": "English", "proficiency": "professional"}, {"language": "Hindi", "proficiency": "professional"}], "redrob_signals": {"profile_completeness_score": 71.5, "signup_date": "2026-03-05", "last_active_date": "2026-05-17", "open_to_work_flag": true, "profile_views_received_30d": 59, "applications_submitted_30d": 6, "recruiter_response_rate": 0.81, "avg_response_time_hours": 72.8, "skill_assessment_scores": {"LangChain": 90.1}, "connection_count": 1465, "endorsements_received": 172, "notice_period_days": 60, "expected_salary_range_inr_lpa": {"min": 37.0, "max": 63.0}, "preferred_work_mode": "onsite", "willing_to_relocate": false, "github_activity_score": 75.8, "search_appearance_30d": 605, "saved_by_recruiters_30d": 40, "interview_completion_rate": 0.97, "offer_acceptance_rate": 0.62, "verified_email": true, "verified_phone": true, "linkedin_connected": false}}
{"candidate_id": "CAND_0020708", "profile": {"anonymized_name": "Kiara Patel", "headline": "Search Engineer | ML, NLP, Recommendation Systems", "summary": "Machine learning engineer with 4.2 years of experience building ML-powered features in production. Strong background in NLP, recommendation systems, and applied AI; comfortable across the ML stack from feature engineering through deployment. Recently, I shipped our first RAG-based feature this year and now own the eval framework for it. I care a lot about evaluation rigor \u2014 too many teams ship models without offline benchmarks they trust. My academic background is in CS/ML but my main learning has come from shipping real systems and seeing what holds up under production load. Open to senior IC roles in applied ML or AI engineering, ideally at product companies where I'd own a meaningful piece of the ML stack.", "location": "Indore, Madhya Pradesh", "country": "India", "years_of_experience": 4.2, "current_title": "Search Engineer", "current_company": "PolicyBazaar", "current_company_size": "5001-10000", "current_industry": "Insurance Tech"}, "career_history": [{"company": "PolicyBazaar", "title": "Search Engineer", "start_date": "2022-04-18", "end_date": null, "duration_months": 50, "is_current": true, "industry": "Insurance Tech", "company_size": "5001-10000", "description": "Developed a semantic search feature for an internal knowledge base of ~500K documents. Used sentence-transformers (all-MiniLM-L6-v2 initially, later upgraded to bge-base) with FAISS for fast nearest-neighbor retrieval. Designed the query expansion module that handles vocabulary mismatch between user queries and document terms. 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{"candidate_id": "CAND_0006418", "profile": {"anonymized_name": "Rahul Mukherjee", "headline": "Machine Learning Engineer | Search, Ranking & Retrieval", "summary": "Machine learning engineer with 5.7 years of experience building ML-powered features in production. Strong background in NLP, recommendation systems, and applied AI; comfortable across the ML stack from feature engineering through deployment. Recently, I shipped our first RAG-based feature this year and now own the eval framework for it. I care a lot about evaluation rigor \u2014 too many teams ship models without offline benchmarks they trust. My academic background is in CS/ML but my main learning has come from shipping real systems and seeing what holds up under production load. 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Worked closely with PMs to define the optimization target (click-through vs. dwell time vs. downstream conversion) \u2014 that work was as important as the modeling itself."}, {"company": "Flipkart", "title": "AI Engineer", "start_date": "2020-11-24", "end_date": "2023-02-12", "duration_months": 27, "is_current": false, "industry": "E-commerce", "company_size": "10001+", "description": "Built and operated production ML pipelines using MLflow for experiment tracking, Kubeflow for orchestration, and our internal feature store. My main project was a churn prediction model that's now used by the customer success team to prioritize outreach. Designed the model monitoring stack: data drift detection, prediction distribution checks, and alerting. Mentored a junior engineer through their first end-to-end ML project last year."}], "education": [{"institution": "Stanford University", "degree": "M.S.", "field_of_study": "Data Science", "start_year": 2014, "end_year": 2017, "grade": "73%", "tier": "tier_1"}], "skills": [{"name": "Kubernetes", "proficiency": "intermediate", "endorsements": 2, "duration_months": 17}, {"name": "gRPC", "proficiency": "intermediate", "endorsements": 14, "duration_months": 26}, {"name": "Semantic Search", "proficiency": "expert", "endorsements": 30, "duration_months": 69}, {"name": "Embeddings", "proficiency": "expert", "endorsements": 43, "duration_months": 71}, {"name": "TensorFlow", "proficiency": "advanced", "endorsements": 30, "duration_months": 48}, {"name": "Object Detection", "proficiency": "intermediate", "endorsements": 10, "duration_months": 31}, {"name": "Weaviate", "proficiency": "expert", "endorsements": 9, "duration_months": 93}, {"name": "Elasticsearch", "proficiency": "advanced", "endorsements": 59, "duration_months": 44}, {"name": "Snowflake", "proficiency": "intermediate", "endorsements": 0, "duration_months": 34}, {"name": "MLflow", "proficiency": "advanced", "endorsements": 30, "duration_months": 25}, {"name": "Learning to Rank", "proficiency": "expert", "endorsements": 1, "duration_months": 53}, {"name": "Forecasting", "proficiency": "intermediate", "endorsements": 8, "duration_months": 8}, {"name": "Qdrant", "proficiency": "expert", "endorsements": 49, "duration_months": 53}, {"name": "Diffusion Models", "proficiency": "advanced", "endorsements": 21, "duration_months": 24}, {"name": "Time Series", "proficiency": "advanced", "endorsements": 53, "duration_months": 21}, {"name": "OpenSearch", "proficiency": "expert", "endorsements": 55, "duration_months": 48}], "certifications": [], "languages": [{"language": "English", "proficiency": "professional"}, {"language": "Hindi", "proficiency": "native"}], "redrob_signals": {"profile_completeness_score": 73.5, "signup_date": "2026-03-18", "last_active_date": "2026-03-31", "open_to_work_flag": true, "profile_views_received_30d": 151, "applications_submitted_30d": 9, "recruiter_response_rate": 0.92, "avg_response_time_hours": 47.0, "skill_assessment_scores": {"Semantic Search": 89.0, "Embeddings": 88.9}, "connection_count": 1119, "endorsements_received": 10, "notice_period_days": 60, "expected_salary_range_inr_lpa": {"min": 41.9, "max": 62.9}, "preferred_work_mode": "remote", "willing_to_relocate": true, "github_activity_score": 59.7, "search_appearance_30d": 372, "saved_by_recruiters_30d": 9, "interview_completion_rate": 0.89, "offer_acceptance_rate": 0.51, "verified_email": true, "verified_phone": true, "linkedin_connected": false}}
{"candidate_id": "CAND_0051615", "profile": {"anonymized_name": "Pooja Banerjee", "headline": "Search Engineer | Applied ML | Building intelligent products", "summary": "Machine learning engineer with 4.6 years of experience building ML-powered features in production. Strong background in NLP, recommendation systems, and applied AI; comfortable across the ML stack from feature engineering through deployment. Recently, my main project for the last 18 months has been the recommendation system that powers our discovery feed. I care a lot about evaluation rigor \u2014 too many teams ship models without offline benchmarks they trust. My academic background is in CS/ML but my main learning has come from shipping real systems and seeing what holds up under production load. 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Final model improved revenue-per-search by 12%."}], "education": [{"institution": "IIIT Hyderabad", "degree": "M.Sc", "field_of_study": "Information Technology", "start_year": 2003, "end_year": 2008, "grade": "65%", "tier": "tier_1"}], "skills": [{"name": "RAG", "proficiency": "expert", "endorsements": 20, "duration_months": 84}, {"name": "HTML", "proficiency": "intermediate", "endorsements": 4, "duration_months": 8}, {"name": "Data Science", "proficiency": "intermediate", "endorsements": 8, "duration_months": 26}, {"name": "Milvus", "proficiency": "advanced", "endorsements": 40, "duration_months": 48}, {"name": "TTS", "proficiency": "advanced", "endorsements": 23, "duration_months": 44}, {"name": "Sentence Transformers", "proficiency": "expert", "endorsements": 31, "duration_months": 37}, {"name": "Fine-tuning LLMs", "proficiency": "expert", "endorsements": 50, "duration_months": 43}, {"name": "Prompt Engineering", "proficiency": "advanced", "endorsements": 48, "duration_months": 30}, {"name": "Vector Search", "proficiency": "advanced", "endorsements": 37, "duration_months": 18}, {"name": "Haystack", "proficiency": "advanced", "endorsements": 14, "duration_months": 51}, {"name": "Computer Vision", "proficiency": "advanced", "endorsements": 5, "duration_months": 25}, {"name": "REST APIs", "proficiency": "intermediate", "endorsements": 11, "duration_months": 10}, {"name": "Webpack", "proficiency": "intermediate", "endorsements": 4, "duration_months": 18}, {"name": "LLMs", "proficiency": "advanced", "endorsements": 5, "duration_months": 41}, {"name": "NLP", "proficiency": "advanced", "endorsements": 17, "duration_months": 22}], "certifications": [{"name": "Google Cloud Professional ML Engineer", "issuer": "Google Cloud", "year": 2022}], "languages": [{"language": "English", "proficiency": "native"}, {"language": "Hindi", "proficiency": "conversational"}], "redrob_signals": {"profile_completeness_score": 75.3, "signup_date": "2024-12-22", "last_active_date": "2026-03-13", "open_to_work_flag": true, "profile_views_received_30d": 215, "applications_submitted_30d": 7, "recruiter_response_rate": 0.88, "avg_response_time_hours": 79.3, "skill_assessment_scores": {}, "connection_count": 1385, "endorsements_received": 66, "notice_period_days": 60, "expected_salary_range_inr_lpa": {"min": 39.9, "max": 46.3}, "preferred_work_mode": "remote", "willing_to_relocate": false, "github_activity_score": 94.6, "search_appearance_30d": 693, "saved_by_recruiters_30d": 42, "interview_completion_rate": 0.89, "offer_acceptance_rate": 0.41, "verified_email": true, "verified_phone": true, "linkedin_connected": true}}
{"candidate_id": "CAND_0064904", "profile": {"anonymized_name": "Karan Trivedi", "headline": "AI Engineer | ML, NLP, Recommendation Systems", "summary": "Machine learning engineer with 4.9 years of experience building ML-powered features in production. Strong background in NLP, recommendation systems, and applied AI; comfortable across the ML stack from feature engineering through deployment. Recently, I led the team that migrated our keyword-search-based product to embedding-based retrieval. I've spent enough time debugging production ranking issues to know which signals matter and which are noise. My academic background is in CS/ML but my main learning has come from shipping real systems and seeing what holds up under production load. Open to senior IC roles in applied ML or AI engineering, ideally at product companies where I'd own a meaningful piece of the ML stack.", "location": "Hyderabad, Telangana", "country": "India", "years_of_experience": 4.9, "current_title": "AI Engineer", "current_company": "LinkedIn", "current_company_size": "10001+", "current_industry": "Internet"}, "career_history": [{"company": "LinkedIn", "title": "AI Engineer", "start_date": "2024-03-08", "end_date": null, "duration_months": 27, "is_current": true, "industry": "Internet", "company_size": "10001+", "description": "Built and operated production ML pipelines using MLflow for experiment tracking, Kubeflow for orchestration, and our internal feature store. My main project was a churn prediction model that's now used by the customer success team to prioritize outreach. Designed the model monitoring stack: data drift detection, prediction distribution checks, and alerting. Mentored a junior engineer through their first end-to-end ML project last year."}, {"company": "Freshworks", "title": "Applied ML Engineer", "start_date": "2021-08-21", "end_date": "2024-03-08", "duration_months": 31, "is_current": false, "industry": "SaaS", "company_size": "5001-10000", "description": "Built a content recommendation system serving 10M+ users that combined collaborative filtering with content-based ranking. The system uses item-item similarity (via sentence-transformer embeddings) for cold starts and a gradient-boosted model trained on engagement signals for warm users. Most of my time went into the feature pipeline (~200 features) and the A/B testing infrastructure. The launch improved 7-day retention by 6% and time spent per session by 14%."}], "education": [{"institution": "RV College of Engineering", "degree": "B.Tech", "field_of_study": "Information Technology", "start_year": 2005, "end_year": 2009, "grade": "9.12 CGPA", "tier": "tier_2"}, {"institution": "Jadavpur University", "degree": "M.E.", "field_of_study": "Data Science", "start_year": 2016, "end_year": 2020, "grade": "8.92 CGPA", "tier": "tier_2"}], "skills": [{"name": "Embeddings", "proficiency": "expert", "endorsements": 58, "duration_months": 74}, {"name": "Hugging Face Transformers", "proficiency": "advanced", "endorsements": 59, "duration_months": 36}, {"name": "Elasticsearch", "proficiency": "expert", "endorsements": 20, "duration_months": 71}, {"name": "Diffusion Models", "proficiency": "intermediate", "endorsements": 6, "duration_months": 35}, {"name": "Forecasting", "proficiency": "advanced", "endorsements": 52, "duration_months": 58}, {"name": "FastAPI", "proficiency": "intermediate", "endorsements": 15, "duration_months": 27}, {"name": "Prompt Engineering", "proficiency": "expert", "endorsements": 39, "duration_months": 81}, {"name": "LLMs", "proficiency": "expert", "endorsements": 24, "duration_months": 63}, {"name": "Python", "proficiency": "expert", "endorsements": 47, "duration_months": 52}, {"name": "MLOps", "proficiency": "advanced", "endorsements": 42, "duration_months": 48}, {"name": "TensorFlow", "proficiency": "expert", "endorsements": 32, "duration_months": 91}, {"name": "Weaviate", "proficiency": "advanced", "endorsements": 19, "duration_months": 33}, {"name": "Vector Search", "proficiency": "advanced", "endorsements": 15, "duration_months": 54}, {"name": "Recommendation Systems", "proficiency": "advanced", "endorsements": 26, "duration_months": 39}, {"name": "OpenSearch", "proficiency": "advanced", "endorsements": 35, "duration_months": 21}, {"name": "Computer Vision", "proficiency": "intermediate", "endorsements": 6, "duration_months": 22}], "certifications": [], "languages": [{"language": "English", "proficiency": "professional"}, {"language": "Hindi", "proficiency": "native"}], "redrob_signals": {"profile_completeness_score": 69.6, "signup_date": "2026-05-10", "last_active_date": "2026-03-17", "open_to_work_flag": true, "profile_views_received_30d": 92, "applications_submitted_30d": 20, "recruiter_response_rate": 0.78, "avg_response_time_hours": 40.8, "skill_assessment_scores": {"Embeddings": 62.2, "Hugging Face Transformers": 53.9, "Elasticsearch": 81.8, "Forecasting": 82.5, "Prompt Engineering": 69.8}, "connection_count": 1102, "endorsements_received": 119, "notice_period_days": 90, "expected_salary_range_inr_lpa": {"min": 29.9, "max": 47.5}, "preferred_work_mode": "flexible", "willing_to_relocate": false, "github_activity_score": 59.0, "search_appearance_30d": 942, "saved_by_recruiters_30d": 39, "interview_completion_rate": 0.85, "offer_acceptance_rate": 0.44, "verified_email": true, "verified_phone": true, "linkedin_connected": false}}
{"candidate_id": "CAND_0050454", "profile": {"anonymized_name": "Saanvi Bansal", "headline": "AI Engineer | Applied ML | Building intelligent products", "summary": "Machine learning engineer with 6.8 years of experience building ML-powered features in production. Strong background in NLP, recommendation systems, and applied AI; comfortable across the ML stack from feature engineering through deployment. Recently, I led the team that migrated our keyword-search-based product to embedding-based retrieval. I care a lot about evaluation rigor \u2014 too many teams ship models without offline benchmarks they trust. My academic background is in CS/ML but my main learning has come from shipping real systems and seeing what holds up under production load. Open to senior IC roles in applied ML or AI engineering, ideally at product companies where I'd own a meaningful piece of the ML stack.", "location": "Delhi, Delhi", "country": "India", "years_of_experience": 6.8, "current_title": "AI Engineer", "current_company": "Rephrase.ai", "current_company_size": "11-50", "current_industry": "AI/ML"}, "career_history": [{"company": "Rephrase.ai", "title": "AI Engineer", "start_date": "2023-12-09", "end_date": null, "duration_months": 30, "is_current": true, "industry": "AI/ML", "company_size": "11-50", "description": "Developed a semantic search feature for an internal knowledge base of ~500K documents. Used sentence-transformers (all-MiniLM-L6-v2 initially, later upgraded to bge-base) with FAISS for fast nearest-neighbor retrieval. Designed the query expansion module that handles vocabulary mismatch between user queries and document terms. Reported search-relevance improvement of 35% over the prior Elasticsearch BM25 setup, validated through human relevance judgments."}, {"company": "Uber", "title": "Machine Learning Engineer", "start_date": "2022-04-18", "end_date": "2023-12-09", "duration_months": 20, "is_current": false, "industry": "Transportation", "company_size": "10001+", "description": "Owned the ranking layer for an e-commerce search product, evolving it from a hand-tuned scoring function to a learning-to-rank model over 9 months. Designed the relevance labeling pipeline (mix of click-through data and explicit human judgments), the feature pipeline, and the training/eval workflow. Most of the work was infrastructure and data quality \u2014 the modeling part was almost the easy bit. Final model improved revenue-per-search by 12%."}, {"company": "Adobe", "title": "Machine Learning Engineer", "start_date": "2019-10-01", "end_date": "2022-04-18", "duration_months": 31, "is_current": false, "industry": "Software", "company_size": "10001+", "description": "Developed a semantic search feature for an internal knowledge base of ~500K documents. Used sentence-transformers (all-MiniLM-L6-v2 initially, later upgraded to bge-base) with FAISS for fast nearest-neighbor retrieval. Designed the query expansion module that handles vocabulary mismatch between user queries and document terms. 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{"candidate_id": "CAND_0074735", "profile": {"anonymized_name": "Ananya Arora", "headline": "Applied ML Engineer | ML, NLP, Recommendation Systems", "summary": "Machine learning engineer with 5.5 years of experience building ML-powered features in production. Strong background in NLP, recommendation systems, and applied AI; comfortable across the ML stack from feature engineering through deployment. Recently, my main project for the last 18 months has been the recommendation system that powers our discovery feed. I've spent enough time debugging production ranking issues to know which signals matter and which are noise. My academic background is in CS/ML but my main learning has come from shipping real systems and seeing what holds up under production load. 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Designed the evaluation framework with both automatic metrics (BLEU, ROUGE) and human-in-the-loop quality scores. Deployment cut average ticket resolution time by 31% for the supported categories."}, {"company": "Rephrase.ai", "title": "Search Engineer", "start_date": "2022-10-31", "end_date": "2024-05-23", "duration_months": 19, "is_current": false, "industry": "AI/ML", "company_size": "11-50", "description": "Implemented a RAG-based customer support chatbot integrated with our existing ticketing system. Built the document ingestion pipeline (chunking, embedding via OpenAI embeddings, storing in Pinecone) and the answer-generation layer (initially GPT-4, then a fine-tuned smaller model for cost control). Designed the evaluation framework with both automatic metrics (BLEU, ROUGE) and human-in-the-loop quality scores. Deployment cut average ticket resolution time by 31% for the supported categories."}, {"company": "Netflix", "title": "NLP Engineer", "start_date": "2021-01-02", "end_date": "2022-10-24", "duration_months": 22, "is_current": false, "industry": "Media", "company_size": "10001+", "description": "Built and operated production ML pipelines using MLflow for experiment tracking, Kubeflow for orchestration, and our internal feature store. My main project was a churn prediction model that's now used by the customer success team to prioritize outreach. Designed the model monitoring stack: data drift detection, prediction distribution checks, and alerting. 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{"candidate_id": "CAND_0058688", "profile": {"anonymized_name": "Anjali Kapoor", "headline": "AI Engineer | Applied ML | Building intelligent products", "summary": "Machine learning engineer with 6.7 years of experience building ML-powered features in production. Strong background in NLP, recommendation systems, and applied AI; comfortable across the ML stack from feature engineering through deployment. Recently, my main project for the last 18 months has been the recommendation system that powers our discovery feed. Along the way I've gotten comfortable with the operational side \u2014 A/B testing, drift monitoring, retraining schedules. My academic background is in CS/ML but my main learning has come from shipping real systems and seeing what holds up under production load. 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Mentored a junior engineer through their first end-to-end ML project last year."}, {"company": "Apple", "title": "Recommendation Systems Engineer", "start_date": "2019-10-31", "end_date": "2023-05-13", "duration_months": 43, "is_current": false, "industry": "Consumer Electronics", "company_size": "10001+", "description": "Built a content recommendation system serving 10M+ users that combined collaborative filtering with content-based ranking. The system uses item-item similarity (via sentence-transformer embeddings) for cold starts and a gradient-boosted model trained on engagement signals for warm users. Most of my time went into the feature pipeline (~200 features) and the A/B testing infrastructure. 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{"candidate_id": "CAND_0049538", "profile": {"anonymized_name": "Sanjay Bose", "headline": "Applied ML Engineer | Search, Ranking & Retrieval", "summary": "Machine learning engineer with 5.8 years of experience building ML-powered features in production. Strong background in NLP, recommendation systems, and applied AI; comfortable across the ML stack from feature engineering through deployment. Recently, I led the team that migrated our keyword-search-based product to embedding-based retrieval. I've spent enough time debugging production ranking issues to know which signals matter and which are noise. My academic background is in CS/ML but my main learning has come from shipping real systems and seeing what holds up under production load. 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Final model improved revenue-per-search by 12%."}, {"company": "PolicyBazaar", "title": "Recommendation Systems Engineer", "start_date": "2021-07-22", "end_date": "2023-02-12", "duration_months": 19, "is_current": false, "industry": "Insurance Tech", "company_size": "5001-10000", "description": "Built and operated production ML pipelines using MLflow for experiment tracking, Kubeflow for orchestration, and our internal feature store. My main project was a churn prediction model that's now used by the customer success team to prioritize outreach. Designed the model monitoring stack: data drift detection, prediction distribution checks, and alerting. Mentored a junior engineer through their first end-to-end ML project last year."}, {"company": "Zoho", "title": "AI Engineer", "start_date": "2020-08-26", "end_date": "2021-05-23", "duration_months": 9, "is_current": false, "industry": "SaaS", "company_size": "10001+", "description": "Built and operated production ML pipelines using MLflow for experiment tracking, Kubeflow for orchestration, and our internal feature store. My main project was a churn prediction model that's now used by the customer success team to prioritize outreach. Designed the model monitoring stack: data drift detection, prediction distribution checks, and alerting. Mentored a junior engineer through their first end-to-end ML project last year."}], "education": [{"institution": "NIT Warangal", "degree": "B.E.", "field_of_study": "Data Science", "start_year": 2004, "end_year": 2009, "grade": "7.07 CGPA", "tier": "tier_1"}], "skills": [{"name": "LlamaIndex", "proficiency": "expert", "endorsements": 26, "duration_months": 41}, {"name": "Feature Engineering", "proficiency": "advanced", "endorsements": 6, "duration_months": 45}, {"name": "MLflow", "proficiency": "advanced", "endorsements": 19, "duration_months": 35}, {"name": "Computer Vision", "proficiency": "intermediate", "endorsements": 12, "duration_months": 19}, {"name": "TTS", "proficiency": "advanced", "endorsements": 54, "duration_months": 44}, {"name": "Learning to Rank", "proficiency": "expert", "endorsements": 17, "duration_months": 47}, {"name": "OpenSearch", "proficiency": "expert", "endorsements": 24, "duration_months": 64}, {"name": "Vector Search", "proficiency": "advanced", "endorsements": 51, "duration_months": 41}, {"name": "Milvus", "proficiency": "expert", "endorsements": 55, "duration_months": 91}, {"name": "QLoRA", "proficiency": "expert", "endorsements": 12, "duration_months": 53}, {"name": "Embeddings", "proficiency": "advanced", "endorsements": 6, "duration_months": 35}, {"name": "CNN", "proficiency": "intermediate", "endorsements": 8, "duration_months": 32}, {"name": "LLMs", "proficiency": "expert", "endorsements": 27, "duration_months": 38}, {"name": "Deep Learning", "proficiency": "advanced", "endorsements": 10, "duration_months": 60}, {"name": "Recommendation Systems", "proficiency": "advanced", "endorsements": 41, "duration_months": 59}, {"name": "Elasticsearch", "proficiency": "expert", "endorsements": 56, "duration_months": 92}, {"name": "PyTorch", "proficiency": "expert", "endorsements": 21, "duration_months": 54}], "certifications": [], "languages": [{"language": "English", "proficiency": "native"}, {"language": "Hindi", "proficiency": "native"}], "redrob_signals": {"profile_completeness_score": 52.9, "signup_date": "2025-12-21", "last_active_date": "2026-05-27", "open_to_work_flag": false, "profile_views_received_30d": 5, "applications_submitted_30d": 0, "recruiter_response_rate": 0.72, "avg_response_time_hours": 27.2, "skill_assessment_scores": {"LlamaIndex": 57.2, "Feature Engineering": 71.4, "MLflow": 86.7, "TTS": 76.9, "Learning to Rank": 60.6}, "connection_count": 1242, "endorsements_received": 79, "notice_period_days": 30, "expected_salary_range_inr_lpa": {"min": 40.0, "max": 64.9}, "preferred_work_mode": "remote", "willing_to_relocate": false, "github_activity_score": 90.8, "search_appearance_30d": 996, "saved_by_recruiters_30d": 60, "interview_completion_rate": 0.67, "offer_acceptance_rate": 0.59, "verified_email": true, "verified_phone": true, "linkedin_connected": true}}
{"candidate_id": "CAND_0081053", "profile": {"anonymized_name": "Om Chopra", "headline": "NLP Engineer | Applied ML | Building intelligent products", "summary": "Machine learning engineer with 5.4 years of experience building ML-powered features in production. Strong background in NLP, recommendation systems, and applied AI; comfortable across the ML stack from feature engineering through deployment. Recently, I built our semantic search infrastructure from scratch \u2014 sentence-transformers, FAISS, the works. I've spent enough time debugging production ranking issues to know which signals matter and which are noise. My academic background is in CS/ML but my main learning has come from shipping real systems and seeing what holds up under production load. 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Mentored a junior engineer through their first end-to-end ML project last year."}, {"company": "Salesforce", "title": "Machine Learning Engineer", "start_date": "2021-02-15", "end_date": "2024-07-29", "duration_months": 42, "is_current": false, "industry": "Software", "company_size": "10001+", "description": "Built a content recommendation system serving 10M+ users that combined collaborative filtering with content-based ranking. The system uses item-item similarity (via sentence-transformer embeddings) for cold starts and a gradient-boosted model trained on engagement signals for warm users. Most of my time went into the feature pipeline (~200 features) and the A/B testing infrastructure. 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{"candidate_id": "CAND_0096142", "profile": {"anonymized_name": "Pranav Sethi", "headline": "Applied ML Engineer | Search, Ranking & Retrieval", "summary": "Machine learning engineer with 5.0 years of experience building ML-powered features in production. Strong background in NLP, recommendation systems, and applied AI; comfortable across the ML stack from feature engineering through deployment. Recently, my main project for the last 18 months has been the recommendation system that powers our discovery feed. Along the way I've gotten comfortable with the operational side \u2014 A/B testing, drift monitoring, retraining schedules. My academic background is in CS/ML but my main learning has come from shipping real systems and seeing what holds up under production load. 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{"candidate_id": "CAND_0042506", "profile": {"anonymized_name": "Zara Pandey", "headline": "Search Engineer | Search, Ranking & Retrieval", "summary": "Machine learning engineer with 4.2 years of experience building ML-powered features in production. Strong background in NLP, recommendation systems, and applied AI; comfortable across the ML stack from feature engineering through deployment. Recently, I built our semantic search infrastructure from scratch \u2014 sentence-transformers, FAISS, the works. I've spent enough time debugging production ranking issues to know which signals matter and which are noise. My academic background is in CS/ML but my main learning has come from shipping real systems and seeing what holds up under production load. 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Designed the evaluation framework with both automatic metrics (BLEU, ROUGE) and human-in-the-loop quality scores. Deployment cut average ticket resolution time by 31% for the supported categories."}, {"company": "Meesho", "title": "Machine Learning Engineer", "start_date": "2022-04-04", "end_date": "2024-11-19", "duration_months": 32, "is_current": false, "industry": "E-commerce", "company_size": "1001-5000", "description": "Built a content recommendation system serving 10M+ users that combined collaborative filtering with content-based ranking. The system uses item-item similarity (via sentence-transformer embeddings) for cold starts and a gradient-boosted model trained on engagement signals for warm users. Most of my time went into the feature pipeline (~200 features) and the A/B testing infrastructure. 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{"candidate_id": "CAND_0075574", "profile": {"anonymized_name": "Karan Ghosh", "headline": "Machine Learning Engineer | ML, NLP, Recommendation Systems", "summary": "Machine learning engineer with 5.7 years of experience building ML-powered features in production. Strong background in NLP, recommendation systems, and applied AI; comfortable across the ML stack from feature engineering through deployment. Recently, I shipped our first RAG-based feature this year and now own the eval framework for it. I've spent enough time debugging production ranking issues to know which signals matter and which are noise. My academic background is in CS/ML but my main learning has come from shipping real systems and seeing what holds up under production load. 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Most of the work was infrastructure and data quality \u2014 the modeling part was almost the easy bit. Final model improved revenue-per-search by 12%."}, {"company": "Observe.AI", "title": "Machine Learning Engineer", "start_date": "2021-06-22", "end_date": "2024-06-06", "duration_months": 36, "is_current": false, "industry": "AI/ML", "company_size": "201-500", "description": "Trained and shipped multiple ranking models for our product's discovery feed using XGBoost and LightGBM. Designed features across three families: content metadata, user behavior signals, and item engagement history. Owned the offline-online correlation analysis that determined which offline metrics actually predicted A/B test outcomes. Worked closely with PMs to define the optimization target (click-through vs. dwell time vs. downstream conversion) \u2014 that work was as important as the modeling itself."}, {"company": "Genpact AI", "title": "NLP Engineer", "start_date": "2020-10-25", "end_date": "2021-06-22", "duration_months": 8, "is_current": false, "industry": "AI Services", "company_size": "10001+", "description": "Implemented a RAG-based customer support chatbot integrated with our existing ticketing system. Built the document ingestion pipeline (chunking, embedding via OpenAI embeddings, storing in Pinecone) and the answer-generation layer (initially GPT-4, then a fine-tuned smaller model for cost control). Designed the evaluation framework with both automatic metrics (BLEU, ROUGE) and human-in-the-loop quality scores. 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{"candidate_id": "CAND_0030031", "profile": {"anonymized_name": "Anil Joshi", "headline": "AI Engineer | ML, NLP, Recommendation Systems", "summary": "Machine learning engineer with 5.7 years of experience building ML-powered features in production. Strong background in NLP, recommendation systems, and applied AI; comfortable across the ML stack from feature engineering through deployment. Recently, I shipped our first RAG-based feature this year and now own the eval framework for it. I've spent enough time debugging production ranking issues to know which signals matter and which are noise. My academic background is in CS/ML but my main learning has come from shipping real systems and seeing what holds up under production load. 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Mentored a junior engineer through their first end-to-end ML project last year."}, {"company": "Google", "title": "Search Engineer", "start_date": "2020-11-17", "end_date": "2023-02-05", "duration_months": 27, "is_current": false, "industry": "Internet", "company_size": "10001+", "description": "Trained and shipped multiple ranking models for our product's discovery feed using XGBoost and LightGBM. Designed features across three families: content metadata, user behavior signals, and item engagement history. Owned the offline-online correlation analysis that determined which offline metrics actually predicted A/B test outcomes. 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{"candidate_id": "CAND_0048558", "profile": {"anonymized_name": "Aisha Joshi", "headline": "Data Scientist | 6.7 yrs in analytics & ML", "summary": "Data scientist / ML engineer with 6.7 years of experience in applied machine learning. Worked across predictive modeling, NLP, analytics, and lightweight deployment workflows. Most of my recent work has been on predictive modeling for customer-facing problems \u2014 churn, conversion, lifetime value. I'm strongest at the modeling and analysis side; comfortable with Python, scikit-learn, pandas, and standard MLOps tooling, but I'm still building depth on the engineering and infra side of production ML. I want to grow into senior AI engineering \u2014 get serious about LLMs and retrieval beyond the surface level.", "location": "Noida, Uttar Pradesh", "country": "India", "years_of_experience": 6.7, "current_title": "Data Scientist", "current_company": "Rephrase.ai", "current_company_size": "11-50", "current_industry": "AI/ML"}, "career_history": [{"company": "Rephrase.ai", "title": "Data Scientist", "start_date": "2023-06-12", "end_date": null, "duration_months": 36, "is_current": true, "industry": "AI/ML", "company_size": "11-50", "description": "Contributed to ML feature engineering and model deployment for a fraud-detection product. My main role was engineering: building the Flask-based prediction API, integrating with the feature store, and writing the model-serving observability layer. I worked closely with senior data scientists but my own modeling work was secondary \u2014 I was the production-side engineer."}, {"company": "Aganitha", "title": "Computer Vision Engineer", "start_date": "2022-01-18", "end_date": "2023-05-13", "duration_months": 16, "is_current": false, "industry": "AI/ML", "company_size": "51-200", "description": "Worked on customer-facing predictive modeling for an e-commerce platform \u2014 churn prediction, conversion likelihood, lifetime value estimation. Used scikit-learn and XGBoost; main models were gradient-boosted trees with ~80 hand-engineered features. The work was split roughly 60/40 between modeling and data prep / SQL. The churn model is now used by the retention team, though my role was more on the modeling side than the productionization."}, {"company": "Paytm", "title": "ML Engineer", "start_date": "2019-10-31", "end_date": "2022-01-18", "duration_months": 27, "is_current": false, "industry": "Fintech", "company_size": "10001+", "description": "Built recommendation-style features at a mid-stage startup \u2014 lighter weight than ranking systems at FAANG, but production. Used a combination of collaborative filtering (matrix factorization in implicit-feedback library) and gradient-boosted re-ranking over engagement signals. Pure ML side of the work; production deployment was handled by the platform team."}], "education": [{"institution": "VIT Vellore", "degree": "M.Sc", "field_of_study": "Computer Science", "start_year": 2002, "end_year": 2006, "grade": "6.70 CGPA", "tier": "tier_2"}], "skills": [{"name": "Information Retrieval", "proficiency": "intermediate", "endorsements": 5, "duration_months": 19}, {"name": "OpenSearch", "proficiency": "advanced", "endorsements": 59, "duration_months": 41}, {"name": "Qdrant", "proficiency": "advanced", "endorsements": 22, "duration_months": 42}, {"name": "GANs", "proficiency": "intermediate", "endorsements": 10, "duration_months": 33}, {"name": "TTS", "proficiency": "intermediate", "endorsements": 14, "duration_months": 16}, {"name": "Spark", "proficiency": "intermediate", "endorsements": 10, "duration_months": 18}, {"name": "Elasticsearch", "proficiency": "intermediate", "endorsements": 15, "duration_months": 18}, {"name": "Feature Engineering", "proficiency": "advanced", "endorsements": 55, "duration_months": 19}, {"name": "Speech Recognition", "proficiency": "advanced", "endorsements": 6, "duration_months": 32}], "certifications": [], "languages": [{"language": "English", "proficiency": "native"}, {"language": "Hindi", "proficiency": "native"}], "redrob_signals": {"profile_completeness_score": 90.0, "signup_date": "2026-04-16", "last_active_date": "2026-05-27", "open_to_work_flag": true, "profile_views_received_30d": 122, "applications_submitted_30d": 6, "recruiter_response_rate": 0.8, "avg_response_time_hours": 11.2, "skill_assessment_scores": {}, "connection_count": 427, "endorsements_received": 24, "notice_period_days": 30, "expected_salary_range_inr_lpa": {"min": 27.9, "max": 41.3}, "preferred_work_mode": "flexible", "willing_to_relocate": true, "github_activity_score": -1, "search_appearance_30d": 713, "saved_by_recruiters_30d": 30, "interview_completion_rate": 0.52, "offer_acceptance_rate": 0.46, "verified_email": true, "verified_phone": true, "linkedin_connected": true}}
{"candidate_id": "CAND_0037160", "profile": {"anonymized_name": "Riya Chatterjee", "headline": "Data Scientist | Building ML-powered solutions", "summary": "Data scientist / ML engineer with 6.0 years of experience in applied machine learning. Worked across predictive modeling, NLP, analytics, and lightweight deployment workflows. I've spent the last couple of years building NLP-based classification and information extraction pipelines. I'm strongest at the modeling and analysis side; comfortable with Python, scikit-learn, pandas, and standard MLOps tooling, but I'm still building depth on the engineering and infra side of production ML. I'm looking to grow into a deeper AI/ML system-building role \u2014 closer to retrieval, LLMs, and modern ranking systems.", "location": "Mumbai, Maharashtra", "country": "India", "years_of_experience": 6.0, "current_title": "Data Scientist", "current_company": "Haptik", "current_company_size": "201-500", "current_industry": "Conversational AI"}, "career_history": [{"company": "Haptik", "title": "Data Scientist", "start_date": "2024-12-03", "end_date": null, "duration_months": 18, "is_current": true, "industry": "Conversational AI", "company_size": "201-500", "description": "Built recommendation-style features at a mid-stage startup \u2014 lighter weight than ranking systems at FAANG, but production. Used a combination of collaborative filtering (matrix factorization in implicit-feedback library) and gradient-boosted re-ranking over engagement signals. Pure ML side of the work; production deployment was handled by the platform team."}, {"company": "TCS", "title": "AI Specialist", "start_date": "2023-07-12", "end_date": "2024-10-04", "duration_months": 15, "is_current": false, "industry": "IT Services", "company_size": "10001+", "description": "Built computer vision models for our product's image moderation feature using PyTorch \u2014 fine-tuned ResNet variants on a labeled dataset of ~200K images. Set up the training pipeline (data loading, augmentation, evaluation) and the inference service. Most of my project work has been in CV; I'm now interested in transitioning toward NLP/LLM work but my professional experience there is limited."}, {"company": "Verloop.io", "title": "Senior Software Engineer (ML)", "start_date": "2020-05-21", "end_date": "2023-07-05", "duration_months": 38, "is_current": false, "industry": "Conversational AI", "company_size": "51-200", "description": "Worked on customer-facing predictive modeling for an e-commerce platform \u2014 churn prediction, conversion likelihood, lifetime value estimation. Used scikit-learn and XGBoost; main models were gradient-boosted trees with ~80 hand-engineered features. The work was split roughly 60/40 between modeling and data prep / SQL. The churn model is now used by the retention team, though my role was more on the modeling side than the productionization."}], "education": [{"institution": "IIT Kanpur", "degree": "Ph.D", "field_of_study": "Data Science", "start_year": 2004, "end_year": 2009, "grade": "9.36 CGPA", "tier": "tier_1"}, {"institution": "Massachusetts Institute of Technology", "degree": "B.Sc", "field_of_study": "Data Science", "start_year": 2003, "end_year": 2006, "grade": "79%", "tier": "tier_1"}], "skills": [{"name": "TTS", "proficiency": "advanced", "endorsements": 60, "duration_months": 57}, {"name": "Deep Learning", "proficiency": "intermediate", "endorsements": 4, "duration_months": 9}, {"name": "LLMs", "proficiency": "advanced", "endorsements": 24, "duration_months": 41}, {"name": "Weaviate", "proficiency": "advanced", "endorsements": 28, "duration_months": 54}, {"name": "Computer Vision", "proficiency": "intermediate", "endorsements": 13, "duration_months": 36}, {"name": "Vector Search", "proficiency": "intermediate", "endorsements": 1, "duration_months": 30}, {"name": "Vue.js", "proficiency": "intermediate", "endorsements": 13, "duration_months": 30}, {"name": "MLflow", "proficiency": "intermediate", "endorsements": 7, "duration_months": 9}, {"name": "Object Detection", "proficiency": "intermediate", "endorsements": 10, "duration_months": 31}, {"name": "Statistical Modeling", "proficiency": "intermediate", "endorsements": 6, "duration_months": 15}, {"name": "Hugging Face Transformers", "proficiency": "advanced", "endorsements": 30, "duration_months": 51}, {"name": "ASR", "proficiency": "intermediate", "endorsements": 9, "duration_months": 11}, {"name": "YOLO", "proficiency": "advanced", "endorsements": 54, "duration_months": 55}, {"name": "Embeddings", "proficiency": "advanced", "endorsements": 46, "duration_months": 21}, {"name": "Image Classification", "proficiency": "advanced", "endorsements": 53, "duration_months": 35}, {"name": "Semantic Search", "proficiency": "intermediate", "endorsements": 1, "duration_months": 15}], "certifications": [], "languages": [{"language": "English", "proficiency": "professional"}, {"language": "Hindi", "proficiency": "professional"}], "redrob_signals": {"profile_completeness_score": 61.9, "signup_date": "2024-06-10", "last_active_date": "2026-03-28", "open_to_work_flag": true, "profile_views_received_30d": 100, "applications_submitted_30d": 9, "recruiter_response_rate": 0.74, "avg_response_time_hours": 78.2, "skill_assessment_scores": {}, "connection_count": 87, "endorsements_received": 117, "notice_period_days": 30, "expected_salary_range_inr_lpa": {"min": 23.4, "max": 43.9}, "preferred_work_mode": "hybrid", "willing_to_relocate": false, "github_activity_score": 28.0, "search_appearance_30d": 116, "saved_by_recruiters_30d": 9, "interview_completion_rate": 1.0, "offer_acceptance_rate": -1, "verified_email": true, "verified_phone": true, "linkedin_connected": false}}
{"candidate_id": "CAND_0097176", "profile": {"anonymized_name": "Advik Mehta", "headline": "ML Engineer | 5.9 yrs in analytics & ML", "summary": "Data scientist / ML engineer with 5.9 years of experience in applied machine learning. Worked across predictive modeling, NLP, analytics, and lightweight deployment workflows. Most of my recent work has been on predictive modeling for customer-facing problems \u2014 churn, conversion, lifetime value. I'm strongest at the modeling and analysis side; comfortable with Python, scikit-learn, pandas, and standard MLOps tooling, but I'm still building depth on the engineering and infra side of production ML. I'm looking to grow into a deeper AI/ML system-building role \u2014 closer to retrieval, LLMs, and modern ranking systems.", "location": "Gurgaon, Haryana", "country": "India", "years_of_experience": 5.9, "current_title": "ML Engineer", "current_company": "TCS", "current_company_size": "10001+", "current_industry": "IT Services"}, "career_history": [{"company": "TCS", "title": "ML Engineer", "start_date": "2025-05-02", "end_date": null, "duration_months": 13, "is_current": true, "industry": "IT Services", "company_size": "10001+", "description": "Built NLP pipelines for sentiment analysis and document classification \u2014 primarily for an internal feedback-analytics dashboard. Started with sklearn-based bag-of-words models, then moved to transformer-based classifiers (DistilBERT) for the harder classes. Comfortable with PyTorch and Hugging Face but most of my training experience has been on small datasets and pre-trained model fine-tuning, not from-scratch model design."}, {"company": "Haptik", "title": "AI Research Engineer", "start_date": "2022-01-04", "end_date": "2025-04-18", "duration_months": 40, "is_current": false, "industry": "Conversational AI", "company_size": "201-500", "description": "Worked on customer-facing predictive modeling for an e-commerce platform \u2014 churn prediction, conversion likelihood, lifetime value estimation. Used scikit-learn and XGBoost; main models were gradient-boosted trees with ~80 hand-engineered features. The work was split roughly 60/40 between modeling and data prep / SQL. The churn model is now used by the retention team, though my role was more on the modeling side than the productionization."}, {"company": "Rephrase.ai", "title": "Junior ML Engineer", "start_date": "2020-09-11", "end_date": "2022-01-04", "duration_months": 16, "is_current": false, "industry": "AI/ML", "company_size": "11-50", "description": "Built recommendation-style features at a mid-stage startup \u2014 lighter weight than ranking systems at FAANG, but production. Used a combination of collaborative filtering (matrix factorization in implicit-feedback library) and gradient-boosted re-ranking over engagement signals. Pure ML side of the work; production deployment was handled by the platform team."}], "education": [{"institution": "VJTI Mumbai", "degree": "Ph.D", "field_of_study": "Computer Engineering", "start_year": 2006, "end_year": 2010, "grade": "6.78 CGPA", "tier": "tier_2"}, {"institution": "SRM Chennai", "degree": "M.E.", "field_of_study": "Data Science", "start_year": 2008, "end_year": 2012, "grade": "9.39 CGPA", "tier": "tier_3"}], "skills": [{"name": "Angular", "proficiency": "beginner", "endorsements": 12, "duration_months": 13}, {"name": "FAISS", "proficiency": "intermediate", "endorsements": 4, "duration_months": 15}, {"name": "Speech Recognition", "proficiency": "advanced", "endorsements": 3, "duration_months": 33}, {"name": "LlamaIndex", "proficiency": "advanced", "endorsements": 45, "duration_months": 32}, {"name": "GANs", "proficiency": "advanced", "endorsements": 8, "duration_months": 26}, {"name": "Sales", "proficiency": "intermediate", "endorsements": 5, "duration_months": 14}, {"name": "CNN", "proficiency": "intermediate", "endorsements": 9, "duration_months": 13}, {"name": "GraphQL", "proficiency": "intermediate", "endorsements": 13, "duration_months": 19}, {"name": "PyTorch", "proficiency": "advanced", "endorsements": 2, "duration_months": 43}, {"name": "LangChain", "proficiency": "advanced", "endorsements": 46, "duration_months": 54}, {"name": "Image Classification", "proficiency": "intermediate", "endorsements": 8, "duration_months": 11}], "certifications": [], "languages": [{"language": "English", "proficiency": "professional"}, {"language": "Hindi", "proficiency": "native"}], "redrob_signals": {"profile_completeness_score": 74.2, "signup_date": "2024-06-05", "last_active_date": "2026-04-05", "open_to_work_flag": true, "profile_views_received_30d": 174, "applications_submitted_30d": 16, "recruiter_response_rate": 0.76, "avg_response_time_hours": 21.1, "skill_assessment_scores": {"Speech Recognition": 86.1, "LlamaIndex": 84.4, "GANs": 68.9, "PyTorch": 52.8}, "connection_count": 292, "endorsements_received": 111, "notice_period_days": 30, "expected_salary_range_inr_lpa": {"min": 26.4, "max": 49.8}, "preferred_work_mode": "flexible", "willing_to_relocate": false, "github_activity_score": 45.4, "search_appearance_30d": 112, "saved_by_recruiters_30d": 15, "interview_completion_rate": 0.87, "offer_acceptance_rate": 0.81, "verified_email": true, "verified_phone": true, "linkedin_connected": true}}
{"candidate_id": "CAND_0039383", "profile": {"anonymized_name": "Riya Malhotra", "headline": "Applied ML Engineer | Applied ML | Building intelligent products", "summary": "Machine learning engineer with 7.1 years of experience building ML-powered features in production. Strong background in NLP, recommendation systems, and applied AI; comfortable across the ML stack from feature engineering through deployment. Recently, I built our semantic search infrastructure from scratch \u2014 sentence-transformers, FAISS, the works. I've learned that most retrieval problems are actually evaluation problems in disguise. My academic background is in CS/ML but my main learning has come from shipping real systems and seeing what holds up under production load. Open to senior IC roles in applied ML or AI engineering, ideally at product companies where I'd own a meaningful piece of the ML stack.", "location": "Gurgaon, Haryana", "country": "India", "years_of_experience": 7.1, "current_title": "Applied ML Engineer", "current_company": "Meesho", "current_company_size": "1001-5000", "current_industry": "E-commerce"}, "career_history": [{"company": "Meesho", "title": "Applied ML Engineer", "start_date": "2023-08-11", "end_date": null, "duration_months": 34, "is_current": true, "industry": "E-commerce", "company_size": "1001-5000", "description": "Trained and shipped multiple ranking models for our product's discovery feed using XGBoost and LightGBM. Designed features across three families: content metadata, user behavior signals, and item engagement history. Owned the offline-online correlation analysis that determined which offline metrics actually predicted A/B test outcomes. Worked closely with PMs to define the optimization target (click-through vs. dwell time vs. downstream conversion) \u2014 that work was as important as the modeling itself."}, {"company": "Swiggy", "title": "Machine Learning Engineer", "start_date": "2022-07-17", "end_date": "2023-08-11", "duration_months": 13, "is_current": false, "industry": "Food Delivery", "company_size": "5001-10000", "description": "Developed a semantic search feature for an internal knowledge base of ~500K documents. Used sentence-transformers (all-MiniLM-L6-v2 initially, later upgraded to bge-base) with FAISS for fast nearest-neighbor retrieval. Designed the query expansion module that handles vocabulary mismatch between user queries and document terms. Reported search-relevance improvement of 35% over the prior Elasticsearch BM25 setup, validated through human relevance judgments."}, {"company": "Paytm", "title": "NLP Engineer", "start_date": "2019-07-03", "end_date": "2022-07-17", "duration_months": 37, "is_current": false, "industry": "Fintech", "company_size": "10001+", "description": "Trained and shipped multiple ranking models for our product's discovery feed using XGBoost and LightGBM. Designed features across three families: content metadata, user behavior signals, and item engagement history. Owned the offline-online correlation analysis that determined which offline metrics actually predicted A/B test outcomes. Worked closely with PMs to define the optimization target (click-through vs. dwell time vs. downstream conversion) \u2014 that work was as important as the modeling itself."}], "education": [{"institution": "IISc Bangalore", "degree": "B.Sc", "field_of_study": "Artificial Intelligence", "start_year": 2009, "end_year": 2012, "grade": "8.09 CGPA", "tier": "tier_1"}], "skills": [{"name": "GANs", "proficiency": "advanced", "endorsements": 0, "duration_months": 39}, {"name": "FAISS", "proficiency": "expert", "endorsements": 38, "duration_months": 67}, {"name": "ASR", "proficiency": "intermediate", "endorsements": 11, "duration_months": 25}, {"name": "Kubeflow", "proficiency": "advanced", "endorsements": 16, "duration_months": 35}, {"name": "Recommendation Systems", "proficiency": "advanced", "endorsements": 47, "duration_months": 24}, {"name": "Hugging Face Transformers", "proficiency": "expert", "endorsements": 8, "duration_months": 58}, {"name": "QLoRA", "proficiency": "expert", "endorsements": 12, "duration_months": 71}, {"name": "Feature Engineering", "proficiency": "intermediate", "endorsements": 1, "duration_months": 20}, {"name": "NLP", "proficiency": "advanced", "endorsements": 59, "duration_months": 23}, {"name": "LoRA", "proficiency": "advanced", "endorsements": 46, "duration_months": 35}, {"name": "Vector Search", "proficiency": "expert", "endorsements": 15, "duration_months": 53}, {"name": "MLflow", "proficiency": "intermediate", "endorsements": 14, "duration_months": 28}, {"name": "Elasticsearch", "proficiency": "expert", "endorsements": 37, "duration_months": 48}, {"name": "Prompt Engineering", "proficiency": "expert", "endorsements": 26, "duration_months": 49}, {"name": "pgvector", "proficiency": "expert", "endorsements": 2, "duration_months": 94}, {"name": "CNN", "proficiency": "advanced", "endorsements": 55, "duration_months": 32}], "certifications": [{"name": "Google Cloud Professional ML Engineer", "issuer": "Google Cloud", "year": 2024}, {"name": "Deep Learning Specialization", "issuer": "Coursera/DeepLearning.AI", "year": 2022}], "languages": [{"language": "English", "proficiency": "native"}, {"language": "Hindi", "proficiency": "conversational"}], "redrob_signals": {"profile_completeness_score": 89.0, "signup_date": "2024-11-11", "last_active_date": "2026-03-19", "open_to_work_flag": true, "profile_views_received_30d": 283, "applications_submitted_30d": 18, "recruiter_response_rate": 0.61, "avg_response_time_hours": 67.6, "skill_assessment_scores": {"GANs": 59.0, "FAISS": 62.8, "Kubeflow": 50.1, "Recommendation Systems": 64.9, "Hugging Face Transformers": 85.8}, "connection_count": 324, "endorsements_received": 16, "notice_period_days": 90, "expected_salary_range_inr_lpa": {"min": 34.9, "max": 39.7}, "preferred_work_mode": "onsite", "willing_to_relocate": true, "github_activity_score": 86.4, "search_appearance_30d": 388, "saved_by_recruiters_30d": 34, "interview_completion_rate": 0.97, "offer_acceptance_rate": 0.78, "verified_email": true, "verified_phone": true, "linkedin_connected": false}}
{"candidate_id": "CAND_0013536", "profile": {"anonymized_name": "Nisha Sen", "headline": "Applied ML Engineer | ML, NLP, Recommendation Systems", "summary": "Machine learning engineer with 4.8 years of experience building ML-powered features in production. Strong background in NLP, recommendation systems, and applied AI; comfortable across the ML stack from feature engineering through deployment. Recently, I led the team that migrated our keyword-search-based product to embedding-based retrieval. I've learned that most retrieval problems are actually evaluation problems in disguise. My academic background is in CS/ML but my main learning has come from shipping real systems and seeing what holds up under production load. Open to senior IC roles in applied ML or AI engineering, ideally at product companies where I'd own a meaningful piece of the ML stack.", "location": "Trivandrum, Kerala", "country": "India", "years_of_experience": 14.1, "current_title": "Applied ML Engineer", "current_company": "Haptik", "current_company_size": "201-500", "current_industry": "Conversational AI"}, "career_history": [{"company": "Haptik", "title": "Applied ML Engineer", "start_date": "2023-08-11", "end_date": null, "duration_months": 34, "is_current": true, "industry": "Conversational AI", "company_size": "201-500", "description": "Trained and shipped multiple ranking models for our product's discovery feed using XGBoost and LightGBM. Designed features across three families: content metadata, user behavior signals, and item engagement history. Owned the offline-online correlation analysis that determined which offline metrics actually predicted A/B test outcomes. Worked closely with PMs to define the optimization target (click-through vs. dwell time vs. downstream conversion) \u2014 that work was as important as the modeling itself."}, {"company": "Rephrase.ai", "title": "Recommendation Systems Engineer", "start_date": "2021-10-13", "end_date": "2023-08-04", "duration_months": 22, "is_current": false, "industry": "AI/ML", "company_size": "11-50", "description": "Trained and shipped multiple ranking models for our product's discovery feed using XGBoost and LightGBM. Designed features across three families: content metadata, user behavior signals, and item engagement history. Owned the offline-online correlation analysis that determined which offline metrics actually predicted A/B test outcomes. 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{"candidate_id": "CAND_0095619", "profile": {"anonymized_name": "Vivaan Dalal", "headline": "NLP Engineer | Applied ML | Building intelligent products", "summary": "Machine learning engineer with 4.2 years of experience building ML-powered features in production. Strong background in NLP, recommendation systems, and applied AI; comfortable across the ML stack from feature engineering through deployment. Recently, I shipped our first RAG-based feature this year and now own the eval framework for it. I've spent enough time debugging production ranking issues to know which signals matter and which are noise. My academic background is in CS/ML but my main learning has come from shipping real systems and seeing what holds up under production load. Open to senior IC roles in applied ML or AI engineering, ideally at product companies where I'd own a meaningful piece of the ML stack.", "location": "Mumbai, Maharashtra", "country": "India", "years_of_experience": 15.6, "current_title": "NLP Engineer", "current_company": "Nykaa", "current_company_size": "1001-5000", "current_industry": "E-commerce"}, "career_history": [{"company": "Nykaa", "title": "NLP Engineer", "start_date": "2022-04-18", "end_date": null, "duration_months": 50, "is_current": true, "industry": "E-commerce", "company_size": "1001-5000", "description": "Owned the ranking layer for an e-commerce search product, evolving it from a hand-tuned scoring function to a learning-to-rank model over 9 months. Designed the relevance labeling pipeline (mix of click-through data and explicit human judgments), the feature pipeline, and the training/eval workflow. Most of the work was infrastructure and data quality \u2014 the modeling part was almost the easy bit. Final model improved revenue-per-search by 12%."}], "education": [{"institution": "NIT Warangal", "degree": "M.Sc", "field_of_study": "Machine Learning", "start_year": 2017, "end_year": 2022, "grade": "76%", "tier": "tier_1"}], "skills": [{"name": "Feature Engineering", "proficiency": "advanced", "endorsements": 45, "duration_months": 41}, {"name": "Pinecone", "proficiency": "expert", "endorsements": 15, "duration_months": 74}, {"name": "MLflow", "proficiency": "advanced", "endorsements": 29, "duration_months": 58}, {"name": "Learning to Rank", "proficiency": "expert", "endorsements": 11, "duration_months": 57}, {"name": "scikit-learn", "proficiency": "expert", "endorsements": 32, "duration_months": 57}, {"name": "Hugging Face Transformers", "proficiency": "advanced", "endorsements": 56, "duration_months": 28}, {"name": "NLP", "proficiency": "expert", "endorsements": 13, "duration_months": 59}, {"name": "Weaviate", "proficiency": "expert", "endorsements": 11, "duration_months": 60}, {"name": "Information Retrieval", "proficiency": "advanced", "endorsements": 8, "duration_months": 52}, {"name": "Time Series", "proficiency": "intermediate", "endorsements": 11, "duration_months": 30}, {"name": "Figma", "proficiency": "intermediate", "endorsements": 12, "duration_months": 16}, {"name": "TTS", "proficiency": "advanced", "endorsements": 13, "duration_months": 58}, {"name": "BM25", "proficiency": "expert", "endorsements": 8, "duration_months": 48}, {"name": "Sentence Transformers", "proficiency": "advanced", "endorsements": 49, "duration_months": 41}, {"name": "Fine-tuning LLMs", "proficiency": "advanced", "endorsements": 41, "duration_months": 21}, {"name": "Elasticsearch", "proficiency": "advanced", "endorsements": 7, "duration_months": 37}, {"name": "YOLO", "proficiency": "advanced", "endorsements": 55, "duration_months": 22}], "certifications": [{"name": "NLP Specialization", "issuer": "Coursera/DeepLearning.AI", "year": 2020}], "languages": [{"language": "English", "proficiency": "professional"}, {"language": "Hindi", "proficiency": "professional"}], "redrob_signals": {"profile_completeness_score": 92.6, "signup_date": "2024-12-31", "last_active_date": "2026-04-16", "open_to_work_flag": true, "profile_views_received_30d": 94, "applications_submitted_30d": 20, "recruiter_response_rate": 0.9, "avg_response_time_hours": 8.6, "skill_assessment_scores": {"Feature Engineering": 50.5, "Pinecone": 77.3, "MLflow": 89.9, "Learning to Rank": 55.6, "scikit-learn": 59.6}, "connection_count": 474, "endorsements_received": 121, "notice_period_days": 30, "expected_salary_range_inr_lpa": {"min": 26.3, "max": 55.1}, "preferred_work_mode": "hybrid", "willing_to_relocate": false, "github_activity_score": 29.7, "search_appearance_30d": 1018, "saved_by_recruiters_30d": 55, "interview_completion_rate": 0.93, "offer_acceptance_rate": 0.64, "verified_email": true, "verified_phone": true, "linkedin_connected": true}}
{"candidate_id": "CAND_0009024", "profile": {"anonymized_name": "Avni Sharma", "headline": "Search Engineer | Applied ML | Building intelligent products", "summary": "Machine learning engineer with 5.2 years of experience building ML-powered features in production. Strong background in NLP, recommendation systems, and applied AI; comfortable across the ML stack from feature engineering through deployment. Recently, I led the team that migrated our keyword-search-based product to embedding-based retrieval. I care a lot about evaluation rigor \u2014 too many teams ship models without offline benchmarks they trust. My academic background is in CS/ML but my main learning has come from shipping real systems and seeing what holds up under production load. 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The launch improved 7-day retention by 6% and time spent per session by 14%."}, {"company": "Aganitha", "title": "Applied ML Engineer", "start_date": "2021-05-23", "end_date": "2023-09-10", "duration_months": 28, "is_current": false, "industry": "AI/ML", "company_size": "51-200", "description": "Built a content recommendation system serving 10M+ users that combined collaborative filtering with content-based ranking. The system uses item-item similarity (via sentence-transformer embeddings) for cold starts and a gradient-boosted model trained on engagement signals for warm users. Most of my time went into the feature pipeline (~200 features) and the A/B testing infrastructure. The launch improved 7-day retention by 6% and time spent per session by 14%."}], "education": [{"institution": "Stanford University", "degree": "M.E.", "field_of_study": "Machine Learning", "start_year": 2010, "end_year": 2015, "grade": "73%", "tier": "tier_1"}], "skills": [{"name": "YOLO", "proficiency": "advanced", "endorsements": 21, "duration_months": 43}, {"name": "Qdrant", "proficiency": "advanced", "endorsements": 48, "duration_months": 31}, {"name": "dbt", "proficiency": "beginner", "endorsements": 5, "duration_months": 2}, {"name": "CSS", "proficiency": "beginner", "endorsements": 15, "duration_months": 10}, {"name": "PEFT", "proficiency": "expert", "endorsements": 58, "duration_months": 43}, {"name": "FAISS", "proficiency": "advanced", "endorsements": 18, "duration_months": 32}, {"name": "Machine Learning", "proficiency": "advanced", "endorsements": 2, "duration_months": 25}, {"name": "Airflow", "proficiency": "beginner", "endorsements": 14, "duration_months": 4}, {"name": "PyTorch", "proficiency": "advanced", "endorsements": 12, "duration_months": 53}, {"name": "TTS", "proficiency": "advanced", "endorsements": 29, "duration_months": 46}, {"name": "MLOps", "proficiency": "advanced", "endorsements": 7, "duration_months": 45}, {"name": "LlamaIndex", "proficiency": "advanced", "endorsements": 37, "duration_months": 47}, {"name": "Recommendation Systems", "proficiency": "expert", "endorsements": 33, "duration_months": 82}, {"name": "MLflow", "proficiency": "advanced", "endorsements": 31, "duration_months": 40}, {"name": "OpenSearch", "proficiency": "expert", "endorsements": 58, "duration_months": 90}, {"name": "LoRA", "proficiency": "expert", "endorsements": 28, "duration_months": 72}], "certifications": [{"name": "Google Cloud Professional ML Engineer", "issuer": "Google Cloud", "year": 2022}], "languages": [{"language": "English", "proficiency": "professional"}, {"language": "Hindi", "proficiency": "professional"}], "redrob_signals": {"profile_completeness_score": 62.7, "signup_date": "2025-02-22", "last_active_date": "2026-04-09", "open_to_work_flag": true, "profile_views_received_30d": 18, "applications_submitted_30d": 4, "recruiter_response_rate": 0.46, "avg_response_time_hours": 18.0, "skill_assessment_scores": {"YOLO": 66.1, "Qdrant": 80.2, "PEFT": 89.8, "FAISS": 81.9, "Machine Learning": 80.7}, "connection_count": 466, "endorsements_received": 171, "notice_period_days": 30, "expected_salary_range_inr_lpa": {"min": 35.9, "max": 48.3}, "preferred_work_mode": "onsite", "willing_to_relocate": false, "github_activity_score": 32.7, "search_appearance_30d": 514, "saved_by_recruiters_30d": 16, "interview_completion_rate": 0.6, "offer_acceptance_rate": -1, "verified_email": true, "verified_phone": true, "linkedin_connected": true}}
{"candidate_id": "CAND_0068811", "profile": {"anonymized_name": "Krishna Mittal", "headline": "Applied ML Engineer | Applied ML | Building intelligent products", "summary": "Machine learning engineer with 8.0 years of experience building ML-powered features in production. Strong background in NLP, recommendation systems, and applied AI; comfortable across the ML stack from feature engineering through deployment. Recently, I led the team that migrated our keyword-search-based product to embedding-based retrieval. I care a lot about evaluation rigor \u2014 too many teams ship models without offline benchmarks they trust. My academic background is in CS/ML but my main learning has come from shipping real systems and seeing what holds up under production load. 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The launch improved 7-day retention by 6% and time spent per session by 14%."}, {"company": "Yellow.ai", "title": "AI Engineer", "start_date": "2022-09-01", "end_date": "2024-10-20", "duration_months": 26, "is_current": false, "industry": "AI/ML", "company_size": "201-500", "description": "Implemented a RAG-based customer support chatbot integrated with our existing ticketing system. Built the document ingestion pipeline (chunking, embedding via OpenAI embeddings, storing in Pinecone) and the answer-generation layer (initially GPT-4, then a fine-tuned smaller model for cost control). Designed the evaluation framework with both automatic metrics (BLEU, ROUGE) and human-in-the-loop quality scores. Deployment cut average ticket resolution time by 31% for the supported categories."}, {"company": "Meesho", "title": "Recommendation Systems Engineer", "start_date": "2020-08-12", "end_date": "2022-09-01", "duration_months": 25, "is_current": false, "industry": "E-commerce", "company_size": "1001-5000", "description": "Owned the ranking layer for an e-commerce search product, evolving it from a hand-tuned scoring function to a learning-to-rank model over 9 months. Designed the relevance labeling pipeline (mix of click-through data and explicit human judgments), the feature pipeline, and the training/eval workflow. Most of the work was infrastructure and data quality \u2014 the modeling part was almost the easy bit. Final model improved revenue-per-search by 12%."}, {"company": "Salesforce", "title": "Search Engineer", "start_date": "2018-05-25", "end_date": "2020-06-13", "duration_months": 25, "is_current": false, "industry": "Software", "company_size": "10001+", "description": "Built and operated production ML pipelines using MLflow for experiment tracking, Kubeflow for orchestration, and our internal feature store. My main project was a churn prediction model that's now used by the customer success team to prioritize outreach. Designed the model monitoring stack: data drift detection, prediction distribution checks, and alerting. 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{"candidate_id": "CAND_0083879", "profile": {"anonymized_name": "Mira Reddy", "headline": "Machine Learning Engineer | Search, Ranking & Retrieval", "summary": "Machine learning engineer with 7.1 years of experience building ML-powered features in production. Strong background in NLP, recommendation systems, and applied AI; comfortable across the ML stack from feature engineering through deployment. Recently, I built our semantic search infrastructure from scratch \u2014 sentence-transformers, FAISS, the works. I've spent enough time debugging production ranking issues to know which signals matter and which are noise. My academic background is in CS/ML but my main learning has come from shipping real systems and seeing what holds up under production load. Open to senior IC roles in applied ML or AI engineering, ideally at product companies where I'd own a meaningful piece of the ML stack.", "location": "Noida, Uttar Pradesh", "country": "India", "years_of_experience": 7.1, "current_title": "Machine Learning Engineer", "current_company": "Ola", "current_company_size": "5001-10000", "current_industry": "Transportation"}, "career_history": [{"company": "Ola", "title": "Machine Learning Engineer", "start_date": "2024-11-03", "end_date": null, "duration_months": 19, "is_current": true, "industry": "Transportation", "company_size": "5001-10000", "description": "Built and operated production ML pipelines using MLflow for experiment tracking, Kubeflow for orchestration, and our internal feature store. My main project was a churn prediction model that's now used by the customer success team to prioritize outreach. Designed the model monitoring stack: data drift detection, prediction distribution checks, and alerting. Mentored a junior engineer through their first end-to-end ML project last year."}, {"company": "PhonePe", "title": "Search Engineer", "start_date": "2021-01-23", "end_date": "2024-11-03", "duration_months": 46, "is_current": false, "industry": "Fintech", "company_size": "5001-10000", "description": "Trained and shipped multiple ranking models for our product's discovery feed using XGBoost and LightGBM. Designed features across three families: content metadata, user behavior signals, and item engagement history. Owned the offline-online correlation analysis that determined which offline metrics actually predicted A/B test outcomes. Worked closely with PMs to define the optimization target (click-through vs. dwell time vs. downstream conversion) \u2014 that work was as important as the modeling itself."}, {"company": "Swiggy", "title": "Senior Data Scientist", "start_date": "2019-07-03", "end_date": "2021-01-23", "duration_months": 19, "is_current": false, "industry": "Food Delivery", "company_size": "5001-10000", "description": "Trained and shipped multiple ranking models for our product's discovery feed using XGBoost and LightGBM. Designed features across three families: content metadata, user behavior signals, and item engagement history. Owned the offline-online correlation analysis that determined which offline metrics actually predicted A/B test outcomes. Worked closely with PMs to define the optimization target (click-through vs. dwell time vs. downstream conversion) \u2014 that work was as important as the modeling itself."}], "education": [{"institution": "NIT Surathkal", "degree": "B.Sc", "field_of_study": "Information Technology", "start_year": 2008, "end_year": 2012, "grade": "84%", "tier": "tier_1"}], "skills": [{"name": "MLOps", "proficiency": "intermediate", "endorsements": 3, "duration_months": 13}, {"name": "Fine-tuning LLMs", "proficiency": "expert", "endorsements": 41, "duration_months": 90}, {"name": "Milvus", "proficiency": "advanced", "endorsements": 32, "duration_months": 47}, {"name": "Image Classification", "proficiency": "intermediate", "endorsements": 14, "duration_months": 25}, {"name": "Data Science", "proficiency": "advanced", "endorsements": 11, "duration_months": 49}, {"name": "scikit-learn", "proficiency": "expert", "endorsements": 19, "duration_months": 39}, {"name": "Hugging Face Transformers", "proficiency": "expert", "endorsements": 51, "duration_months": 38}, {"name": "Prompt Engineering", "proficiency": "expert", "endorsements": 48, "duration_months": 50}, {"name": "Machine Learning", "proficiency": "expert", "endorsements": 6, "duration_months": 47}, {"name": "Sentence Transformers", "proficiency": "expert", "endorsements": 7, "duration_months": 51}], "certifications": [{"name": "AWS Certified Machine Learning Specialty", "issuer": "AWS", "year": 2021}], "languages": [{"language": "English", "proficiency": "professional"}, {"language": "Hindi", "proficiency": "native"}], "redrob_signals": {"profile_completeness_score": 59.6, "signup_date": "2025-06-08", "last_active_date": "2026-04-17", "open_to_work_flag": true, "profile_views_received_30d": 274, "applications_submitted_30d": 14, "recruiter_response_rate": 0.47, "avg_response_time_hours": 24.4, "skill_assessment_scores": {"Fine-tuning LLMs": 63.1}, "connection_count": 1486, "endorsements_received": 140, "notice_period_days": 30, "expected_salary_range_inr_lpa": {"min": 44.7, "max": 62.0}, "preferred_work_mode": "hybrid", "willing_to_relocate": false, "github_activity_score": -1, "search_appearance_30d": 574, "saved_by_recruiters_30d": 64, "interview_completion_rate": 0.83, "offer_acceptance_rate": 0.85, "verified_email": true, "verified_phone": true, "linkedin_connected": true}}
{"candidate_id": "CAND_0083852", "profile": {"anonymized_name": "Reyansh Banerjee", "headline": "Data Scientist | Building ML-powered solutions", "summary": "Data scientist / ML engineer with 6.0 years of experience in applied machine learning. Worked across predictive modeling, NLP, analytics, and lightweight deployment workflows. Most of my recent work has been on predictive modeling for customer-facing problems \u2014 churn, conversion, lifetime value. I'm strongest at the modeling and analysis side; comfortable with Python, scikit-learn, pandas, and standard MLOps tooling, but I'm still building depth on the engineering and infra side of production ML. I'm looking to grow into a deeper AI/ML system-building role \u2014 closer to retrieval, LLMs, and modern ranking systems.", "location": "Ahmedabad, Gujarat", "country": "India", "years_of_experience": 6.0, "current_title": "Data Scientist", "current_company": "Mad Street Den", "current_company_size": "201-500", "current_industry": "AI/ML"}, "career_history": [{"company": "Mad Street Den", "title": "Data Scientist", "start_date": "2023-10-10", "end_date": null, "duration_months": 32, "is_current": true, "industry": "AI/ML", "company_size": "201-500", "description": "Worked on time-series forecasting models for supply-chain demand prediction at a logistics company. Built models in Prophet, LightGBM, and (for one project) a small LSTM \u2014 the LightGBM model ended up shipping. Also ran some reinforcement learning experiments for dynamic pricing but those didn't make it to production. The work was a mix of modeling, analysis, and stakeholder communication with the operations team."}, {"company": "Dream11", "title": "Computer Vision Engineer", "start_date": "2020-07-13", "end_date": "2023-09-26", "duration_months": 39, "is_current": false, "industry": "Gaming", "company_size": "1001-5000", "description": "Built recommendation-style features at a mid-stage startup \u2014 lighter weight than ranking systems at FAANG, but production. Used a combination of collaborative filtering (matrix factorization in implicit-feedback library) and gradient-boosted re-ranking over engagement signals. Pure ML side of the work; production deployment was handled by the platform team."}], "education": [{"institution": "SRM Chennai", "degree": "Ph.D", "field_of_study": "Data Science", "start_year": 2002, "end_year": 2007, "grade": "8.92 CGPA", "tier": "tier_3"}, {"institution": "RV College of Engineering", "degree": "Ph.D", "field_of_study": "Computer Engineering", "start_year": 2008, "end_year": 2013, "grade": "85%", "tier": "tier_2"}], "skills": [{"name": "Qdrant", "proficiency": "intermediate", "endorsements": 14, "duration_months": 32}, {"name": "Kubeflow", "proficiency": "advanced", "endorsements": 3, "duration_months": 54}, {"name": "QLoRA", "proficiency": "intermediate", "endorsements": 5, "duration_months": 17}, {"name": "Feature Engineering", "proficiency": "intermediate", "endorsements": 0, "duration_months": 32}, {"name": "Sales", "proficiency": "intermediate", "endorsements": 0, "duration_months": 10}, {"name": "TTS", "proficiency": "intermediate", "endorsements": 9, "duration_months": 34}, {"name": "Pinecone", "proficiency": "advanced", "endorsements": 4, "duration_months": 59}, {"name": "PyTorch", "proficiency": "intermediate", "endorsements": 9, "duration_months": 35}, {"name": "pgvector", "proficiency": "intermediate", "endorsements": 7, "duration_months": 32}, {"name": "Information Retrieval", "proficiency": "intermediate", "endorsements": 5, "duration_months": 23}, {"name": "Illustrator", "proficiency": "beginner", "endorsements": 14, "duration_months": 9}, {"name": "Computer Vision", "proficiency": "intermediate", "endorsements": 14, "duration_months": 31}, {"name": "Next.js", "proficiency": "intermediate", "endorsements": 12, "duration_months": 34}, {"name": "MLflow", "proficiency": "intermediate", "endorsements": 8, "duration_months": 21}, {"name": "Sentence Transformers", "proficiency": "advanced", "endorsements": 19, "duration_months": 31}, {"name": "Data Science", "proficiency": "intermediate", "endorsements": 12, "duration_months": 9}, {"name": "BentoML", "proficiency": "intermediate", "endorsements": 14, "duration_months": 13}, {"name": "Prompt Engineering", "proficiency": "intermediate", "endorsements": 15, "duration_months": 34}, {"name": "Snowflake", "proficiency": "intermediate", "endorsements": 5, "duration_months": 20}], "certifications": [], "languages": [{"language": "English", "proficiency": "professional"}, {"language": "Hindi", "proficiency": "conversational"}], "redrob_signals": {"profile_completeness_score": 91.5, "signup_date": "2024-11-15", "last_active_date": "2026-05-27", "open_to_work_flag": true, "profile_views_received_30d": 219, "applications_submitted_30d": 10, "recruiter_response_rate": 0.85, "avg_response_time_hours": 7.4, "skill_assessment_scores": {"Kubeflow": 68.6}, "connection_count": 363, "endorsements_received": 126, "notice_period_days": 90, "expected_salary_range_inr_lpa": {"min": 30.2, "max": 53.7}, "preferred_work_mode": "onsite", "willing_to_relocate": true, "github_activity_score": 70.8, "search_appearance_30d": 539, "saved_by_recruiters_30d": 24, "interview_completion_rate": 0.74, "offer_acceptance_rate": 0.57, "verified_email": true, "verified_phone": true, "linkedin_connected": false}}
{"candidate_id": "CAND_0052335", "profile": {"anonymized_name": "Vivaan Sen", "headline": "Applied ML Engineer | Applied ML | Building intelligent products", "summary": "Machine learning engineer with 6.4 years of experience building ML-powered features in production. Strong background in NLP, recommendation systems, and applied AI; comfortable across the ML stack from feature engineering through deployment. Recently, I built our semantic search infrastructure from scratch \u2014 sentence-transformers, FAISS, the works. Along the way I've gotten comfortable with the operational side \u2014 A/B testing, drift monitoring, retraining schedules. My academic background is in CS/ML but my main learning has come from shipping real systems and seeing what holds up under production load. Open to senior IC roles in applied ML or AI engineering, ideally at product companies where I'd own a meaningful piece of the ML stack.", "location": "Berlin", "country": "Germany", "years_of_experience": 6.4, "current_title": "Applied ML Engineer", "current_company": "Aganitha", "current_company_size": "51-200", "current_industry": "AI/ML"}, "career_history": [{"company": "Aganitha", "title": "Applied ML Engineer", "start_date": "2022-10-15", "end_date": null, "duration_months": 44, "is_current": true, "industry": "AI/ML", "company_size": "51-200", "description": "Owned the ranking layer for an e-commerce search product, evolving it from a hand-tuned scoring function to a learning-to-rank model over 9 months. Designed the relevance labeling pipeline (mix of click-through data and explicit human judgments), the feature pipeline, and the training/eval workflow. Most of the work was infrastructure and data quality \u2014 the modeling part was almost the easy bit. Final model improved revenue-per-search by 12%."}, {"company": "Wysa", "title": "AI Engineer", "start_date": "2020-01-29", "end_date": "2022-09-15", "duration_months": 32, "is_current": false, "industry": "HealthTech AI", "company_size": "51-200", "description": "Implemented a RAG-based customer support chatbot integrated with our existing ticketing system. Built the document ingestion pipeline (chunking, embedding via OpenAI embeddings, storing in Pinecone) and the answer-generation layer (initially GPT-4, then a fine-tuned smaller model for cost control). Designed the evaluation framework with both automatic metrics (BLEU, ROUGE) and human-in-the-loop quality scores. Deployment cut average ticket resolution time by 31% for the supported categories."}], "education": [{"institution": "Jadavpur University", "degree": "B.E.", "field_of_study": "Data Science", "start_year": 2003, "end_year": 2007, "grade": "7.78 CGPA", "tier": "tier_2"}], "skills": [{"name": "Diffusion Models", "proficiency": "advanced", "endorsements": 1, "duration_months": 29}, {"name": "PyTorch", "proficiency": "expert", "endorsements": 22, "duration_months": 83}, {"name": "GANs", "proficiency": "advanced", "endorsements": 41, "duration_months": 37}, {"name": "Machine Learning", "proficiency": "expert", "endorsements": 8, "duration_months": 36}, {"name": "Learning to Rank", "proficiency": "expert", "endorsements": 53, "duration_months": 49}, {"name": "NLP", "proficiency": "expert", "endorsements": 15, "duration_months": 79}, {"name": "CNN", "proficiency": "intermediate", "endorsements": 11, "duration_months": 19}, {"name": "Semantic Search", "proficiency": "advanced", "endorsements": 4, "duration_months": 60}, {"name": "TTS", "proficiency": "intermediate", "endorsements": 14, "duration_months": 16}, {"name": "Weights & Biases", "proficiency": "intermediate", "endorsements": 10, "duration_months": 14}, {"name": "Data Science", "proficiency": "intermediate", "endorsements": 15, "duration_months": 32}, {"name": "LlamaIndex", "proficiency": "advanced", "endorsements": 22, "duration_months": 48}, {"name": "Sentence Transformers", "proficiency": "expert", "endorsements": 36, "duration_months": 43}, {"name": "LangChain", "proficiency": "expert", "endorsements": 8, "duration_months": 72}], "certifications": [], "languages": [{"language": "English", "proficiency": "professional"}, {"language": "Hindi", "proficiency": "conversational"}], "redrob_signals": {"profile_completeness_score": 73.9, "signup_date": "2025-08-01", "last_active_date": "2026-04-10", "open_to_work_flag": true, "profile_views_received_30d": 35, "applications_submitted_30d": 18, "recruiter_response_rate": 0.79, "avg_response_time_hours": 27.3, "skill_assessment_scores": {}, "connection_count": 638, "endorsements_received": 45, "notice_period_days": 60, "expected_salary_range_inr_lpa": {"min": 44.8, "max": 38.0}, "preferred_work_mode": "flexible", "willing_to_relocate": true, "github_activity_score": 20.9, "search_appearance_30d": 499, "saved_by_recruiters_30d": 35, "interview_completion_rate": 0.59, "offer_acceptance_rate": 0.77, "verified_email": true, "verified_phone": true, "linkedin_connected": true}}
{"candidate_id": "CAND_0051292", "profile": {"anonymized_name": "Shreya Chatterjee", "headline": "Applied ML Engineer | Applied ML | Building intelligent products", "summary": "Machine learning engineer with 5.2 years of experience building ML-powered features in production. Strong background in NLP, recommendation systems, and applied AI; comfortable across the ML stack from feature engineering through deployment. Recently, my main project for the last 18 months has been the recommendation system that powers our discovery feed. I've spent enough time debugging production ranking issues to know which signals matter and which are noise. My academic background is in CS/ML but my main learning has come from shipping real systems and seeing what holds up under production load. Open to senior IC roles in applied ML or AI engineering, ideally at product companies where I'd own a meaningful piece of the ML stack.", "location": "Trivandrum, Kerala", "country": "India", "years_of_experience": 5.2, "current_title": "Applied ML Engineer", "current_company": "Freshworks", "current_company_size": "5001-10000", "current_industry": "SaaS"}, "career_history": [{"company": "Freshworks", "title": "Applied ML Engineer", "start_date": "2025-02-01", "end_date": null, "duration_months": 16, "is_current": true, "industry": "SaaS", "company_size": "5001-10000", "description": "Owned the ranking layer for an e-commerce search product, evolving it from a hand-tuned scoring function to a learning-to-rank model over 9 months. Designed the relevance labeling pipeline (mix of click-through data and explicit human judgments), the feature pipeline, and the training/eval workflow. Most of the work was infrastructure and data quality \u2014 the modeling part was almost the easy bit. Final model improved revenue-per-search by 12%."}, {"company": "Vedantu", "title": "Search Engineer", "start_date": "2022-10-01", "end_date": "2025-01-18", "duration_months": 28, "is_current": false, "industry": "EdTech", "company_size": "1001-5000", "description": "Developed a semantic search feature for an internal knowledge base of ~500K documents. Used sentence-transformers (all-MiniLM-L6-v2 initially, later upgraded to bge-base) with FAISS for fast nearest-neighbor retrieval. Designed the query expansion module that handles vocabulary mismatch between user queries and document terms. Reported search-relevance improvement of 35% over the prior Elasticsearch BM25 setup, validated through human relevance judgments."}, {"company": "Vedantu", "title": "Search Engineer", "start_date": "2021-06-08", "end_date": "2022-10-01", "duration_months": 16, "is_current": false, "industry": "EdTech", "company_size": "1001-5000", "description": "Implemented a RAG-based customer support chatbot integrated with our existing ticketing system. Built the document ingestion pipeline (chunking, embedding via OpenAI embeddings, storing in Pinecone) and the answer-generation layer (initially GPT-4, then a fine-tuned smaller model for cost control). Designed the evaluation framework with both automatic metrics (BLEU, ROUGE) and human-in-the-loop quality scores. Deployment cut average ticket resolution time by 31% for the supported categories."}], "education": [{"institution": "Manipal Institute of Technology", "degree": "B.Sc", "field_of_study": "Computer Science", "start_year": 2017, "end_year": 2021, "grade": "9.18 CGPA", "tier": "tier_2"}, {"institution": "Jadavpur University", "degree": "B.Tech", "field_of_study": "Computer Science", "start_year": 2009, "end_year": 2013, "grade": "9.31 CGPA", "tier": "tier_2"}], "skills": [{"name": "Vue.js", "proficiency": "beginner", "endorsements": 10, "duration_months": 15}, {"name": "pgvector", "proficiency": "expert", "endorsements": 41, "duration_months": 64}, {"name": "FAISS", "proficiency": "advanced", "endorsements": 50, "duration_months": 21}, {"name": "BentoML", "proficiency": "advanced", "endorsements": 54, "duration_months": 32}, {"name": "LLMs", "proficiency": "advanced", "endorsements": 31, "duration_months": 28}, {"name": "Time Series", "proficiency": "advanced", "endorsements": 57, "duration_months": 39}, {"name": "Vector Search", "proficiency": "expert", "endorsements": 36, "duration_months": 43}, {"name": "PEFT", "proficiency": "advanced", "endorsements": 44, "duration_months": 32}, {"name": "TTS", "proficiency": "advanced", "endorsements": 51, "duration_months": 39}, {"name": "Fine-tuning LLMs", "proficiency": "expert", "endorsements": 44, "duration_months": 48}, {"name": "JavaScript", "proficiency": "beginner", "endorsements": 13, "duration_months": 12}, {"name": "NLP", "proficiency": "expert", "endorsements": 41, "duration_months": 55}, {"name": "RAG", "proficiency": "expert", "endorsements": 59, "duration_months": 86}, {"name": "Elasticsearch", "proficiency": "expert", "endorsements": 26, "duration_months": 39}], "certifications": [], "languages": [{"language": "English", "proficiency": "professional"}, {"language": "Hindi", "proficiency": "professional"}], "redrob_signals": {"profile_completeness_score": 50.6, "signup_date": "2024-06-18", "last_active_date": "2026-04-12", "open_to_work_flag": true, "profile_views_received_30d": 182, "applications_submitted_30d": 19, "recruiter_response_rate": 0.52, "avg_response_time_hours": 14.3, "skill_assessment_scores": {}, "connection_count": 975, "endorsements_received": 139, "notice_period_days": 30, "expected_salary_range_inr_lpa": {"min": 24.0, "max": 53.8}, "preferred_work_mode": "hybrid", "willing_to_relocate": false, "github_activity_score": 61.7, "search_appearance_30d": 56, "saved_by_recruiters_30d": 9, "interview_completion_rate": 0.56, "offer_acceptance_rate": 0.65, "verified_email": true, "verified_phone": true, "linkedin_connected": true}}
{"candidate_id": "CAND_0074225", "profile": {"anonymized_name": "Kabir Agarwal", "headline": "Machine Learning Engineer | ML, NLP, Recommendation Systems", "summary": "Machine learning engineer with 4.3 years of experience building ML-powered features in production. Strong background in NLP, recommendation systems, and applied AI; comfortable across the ML stack from feature engineering through deployment. Recently, my main project for the last 18 months has been the recommendation system that powers our discovery feed. I've learned that most retrieval problems are actually evaluation problems in disguise. My academic background is in CS/ML but my main learning has come from shipping real systems and seeing what holds up under production load. Open to senior IC roles in applied ML or AI engineering, ideally at product companies where I'd own a meaningful piece of the ML stack.", "location": "Vizag, Andhra Pradesh", "country": "India", "years_of_experience": 4.3, "current_title": "Machine Learning Engineer", "current_company": "Unacademy", "current_company_size": "5001-10000", "current_industry": "EdTech"}, "career_history": [{"company": "Unacademy", "title": "Machine Learning Engineer", "start_date": "2024-04-07", "end_date": null, "duration_months": 26, "is_current": true, "industry": "EdTech", "company_size": "5001-10000", "description": "Trained and shipped multiple ranking models for our product's discovery feed using XGBoost and LightGBM. Designed features across three families: content metadata, user behavior signals, and item engagement history. Owned the offline-online correlation analysis that determined which offline metrics actually predicted A/B test outcomes. Worked closely with PMs to define the optimization target (click-through vs. dwell time vs. downstream conversion) \u2014 that work was as important as the modeling itself."}, {"company": "Mad Street Den", "title": "Machine Learning Engineer", "start_date": "2022-03-19", "end_date": "2024-04-07", "duration_months": 25, "is_current": false, "industry": "AI/ML", "company_size": "201-500", "description": "Implemented a RAG-based customer support chatbot integrated with our existing ticketing system. Built the document ingestion pipeline (chunking, embedding via OpenAI embeddings, storing in Pinecone) and the answer-generation layer (initially GPT-4, then a fine-tuned smaller model for cost control). Designed the evaluation framework with both automatic metrics (BLEU, ROUGE) and human-in-the-loop quality scores. Deployment cut average ticket resolution time by 31% for the supported categories."}], "education": [{"institution": "Thapar University", "degree": "M.S.", "field_of_study": "Computer Engineering", "start_year": 2001, "end_year": 2005, "grade": "73%", "tier": "tier_2"}], "skills": [{"name": "Apache Beam", "proficiency": "intermediate", "endorsements": 7, "duration_months": 25}, {"name": "Statistical Modeling", "proficiency": "intermediate", "endorsements": 5, "duration_months": 24}, {"name": "Recommendation Systems", "proficiency": "advanced", "endorsements": 36, "duration_months": 58}, {"name": "Semantic Search", "proficiency": "advanced", "endorsements": 3, "duration_months": 26}, {"name": "ASR", "proficiency": "advanced", "endorsements": 43, "duration_months": 27}, {"name": "Elasticsearch", "proficiency": "expert", "endorsements": 42, "duration_months": 37}, {"name": "Milvus", "proficiency": "expert", "endorsements": 46, "duration_months": 94}, {"name": "Python", "proficiency": "expert", "endorsements": 56, "duration_months": 71}, {"name": "Time Series", "proficiency": "advanced", "endorsements": 18, "duration_months": 59}, {"name": "Haystack", "proficiency": "advanced", "endorsements": 11, "duration_months": 41}, {"name": "scikit-learn", "proficiency": "advanced", "endorsements": 32, "duration_months": 29}, {"name": "Hugging Face Transformers", "proficiency": "expert", "endorsements": 17, "duration_months": 62}, {"name": "Qdrant", "proficiency": "advanced", "endorsements": 22, "duration_months": 57}, {"name": "CI/CD", "proficiency": "beginner", "endorsements": 8, "duration_months": 10}, {"name": "QLoRA", "proficiency": "expert", "endorsements": 14, "duration_months": 51}, {"name": "Machine Learning", "proficiency": "expert", "endorsements": 59, "duration_months": 51}, {"name": "Vector Search", "proficiency": "expert", "endorsements": 45, "duration_months": 90}], "certifications": [], "languages": [{"language": "English", "proficiency": "native"}, {"language": "Hindi", "proficiency": "conversational"}], "redrob_signals": {"profile_completeness_score": 74.0, "signup_date": "2025-09-28", "last_active_date": "2026-05-20", "open_to_work_flag": true, "profile_views_received_30d": 113, "applications_submitted_30d": 11, "recruiter_response_rate": 0.91, "avg_response_time_hours": 34.1, "skill_assessment_scores": {"Recommendation Systems": 58.5, "Semantic Search": 89.2, "ASR": 56.1}, "connection_count": 266, "endorsements_received": 122, "notice_period_days": 120, "expected_salary_range_inr_lpa": {"min": 35.8, "max": 46.7}, "preferred_work_mode": "hybrid", "willing_to_relocate": true, "github_activity_score": 54.3, "search_appearance_30d": 654, "saved_by_recruiters_30d": 53, "interview_completion_rate": 0.8, "offer_acceptance_rate": -1, "verified_email": true, "verified_phone": true, "linkedin_connected": true}}
{"candidate_id": "CAND_0036437", "profile": {"anonymized_name": "Arjun Joshi", "headline": "Search Engineer | Search, Ranking & Retrieval", "summary": "Machine learning engineer with 4.8 years of experience building ML-powered features in production. Strong background in NLP, recommendation systems, and applied AI; comfortable across the ML stack from feature engineering through deployment. Recently, I shipped our first RAG-based feature this year and now own the eval framework for it. I've spent enough time debugging production ranking issues to know which signals matter and which are noise. My academic background is in CS/ML but my main learning has come from shipping real systems and seeing what holds up under production load. Open to senior IC roles in applied ML or AI engineering, ideally at product companies where I'd own a meaningful piece of the ML stack.", "location": "Kolkata, West Bengal", "country": "India", "years_of_experience": 4.8, "current_title": "Search Engineer", "current_company": "Rephrase.ai", "current_company_size": "11-50", "current_industry": "AI/ML"}, "career_history": [{"company": "Rephrase.ai", "title": "Search Engineer", "start_date": "2024-09-04", "end_date": null, "duration_months": 21, "is_current": true, "industry": "AI/ML", "company_size": "11-50", "description": "Owned the ranking layer for an e-commerce search product, evolving it from a hand-tuned scoring function to a learning-to-rank model over 9 months. Designed the relevance labeling pipeline (mix of click-through data and explicit human judgments), the feature pipeline, and the training/eval workflow. Most of the work was infrastructure and data quality \u2014 the modeling part was almost the easy bit. Final model improved revenue-per-search by 12%."}, {"company": "Nykaa", "title": "Machine Learning Engineer", "start_date": "2023-04-29", "end_date": "2024-08-21", "duration_months": 16, "is_current": false, "industry": "E-commerce", "company_size": "1001-5000", "description": "Owned the ranking layer for an e-commerce search product, evolving it from a hand-tuned scoring function to a learning-to-rank model over 9 months. Designed the relevance labeling pipeline (mix of click-through data and explicit human judgments), the feature pipeline, and the training/eval workflow. Most of the work was infrastructure and data quality \u2014 the modeling part was almost the easy bit. Final model improved revenue-per-search by 12%."}, {"company": "Ola", "title": "AI Engineer", "start_date": "2021-09-29", "end_date": "2023-04-22", "duration_months": 19, "is_current": false, "industry": "Transportation", "company_size": "5001-10000", "description": "Built and operated production ML pipelines using MLflow for experiment tracking, Kubeflow for orchestration, and our internal feature store. My main project was a churn prediction model that's now used by the customer success team to prioritize outreach. Designed the model monitoring stack: data drift detection, prediction distribution checks, and alerting. Mentored a junior engineer through their first end-to-end ML project last year."}], "education": [{"institution": "Anna University", "degree": "B.Tech", "field_of_study": "Machine Learning", "start_year": 2007, "end_year": 2011, "grade": "65%", "tier": "tier_2"}], "skills": [{"name": "Go", "proficiency": "intermediate", "endorsements": 5, "duration_months": 28}, {"name": "Kubeflow", "proficiency": "intermediate", "endorsements": 7, "duration_months": 28}, {"name": "OpenSearch", "proficiency": "expert", "endorsements": 32, "duration_months": 48}, {"name": "Elasticsearch", "proficiency": "expert", "endorsements": 11, "duration_months": 92}, {"name": "Feature Engineering", "proficiency": "intermediate", "endorsements": 12, "duration_months": 9}, {"name": "Sentence Transformers", "proficiency": "expert", "endorsements": 4, "duration_months": 93}, {"name": "Milvus", "proficiency": "advanced", "endorsements": 38, "duration_months": 57}, {"name": "Vector Search", "proficiency": "expert", "endorsements": 32, "duration_months": 50}, {"name": "Weaviate", "proficiency": "expert", "endorsements": 11, "duration_months": 73}, {"name": "Accounting", "proficiency": "intermediate", "endorsements": 6, "duration_months": 27}, {"name": "FAISS", "proficiency": "advanced", "endorsements": 60, "duration_months": 37}, {"name": "GANs", "proficiency": "intermediate", "endorsements": 11, "duration_months": 30}], "certifications": [], "languages": [{"language": "English", "proficiency": "professional"}, {"language": "Hindi", "proficiency": "conversational"}], "redrob_signals": {"profile_completeness_score": 93.9, "signup_date": "2025-07-30", "last_active_date": "2026-05-14", "open_to_work_flag": false, "profile_views_received_30d": 241, "applications_submitted_30d": 20, "recruiter_response_rate": 0.87, "avg_response_time_hours": 35.0, "skill_assessment_scores": {"OpenSearch": 81.2, "Elasticsearch": 60.6}, "connection_count": 555, "endorsements_received": 32, "notice_period_days": 30, "expected_salary_range_inr_lpa": {"min": 22.3, "max": 58.7}, "preferred_work_mode": "remote", "willing_to_relocate": true, "github_activity_score": 35.2, "search_appearance_30d": 962, "saved_by_recruiters_30d": 15, "interview_completion_rate": 0.9, "offer_acceptance_rate": 0.51, "verified_email": true, "verified_phone": true, "linkedin_connected": true}}
{"candidate_id": "CAND_0055992", "profile": {"anonymized_name": "Deepak Bansal", "headline": "AI Engineer | Search, Ranking & Retrieval", "summary": "Machine learning engineer with 6.8 years of experience building ML-powered features in production. Strong background in NLP, recommendation systems, and applied AI; comfortable across the ML stack from feature engineering through deployment. Recently, I led the team that migrated our keyword-search-based product to embedding-based retrieval. I care a lot about evaluation rigor \u2014 too many teams ship models without offline benchmarks they trust. My academic background is in CS/ML but my main learning has come from shipping real systems and seeing what holds up under production load. Open to senior IC roles in applied ML or AI engineering, ideally at product companies where I'd own a meaningful piece of the ML stack.", "location": "Sydney", "country": "Australia", "years_of_experience": 16.9, "current_title": "AI Engineer", "current_company": "CRED", "current_company_size": "1001-5000", "current_industry": "Fintech"}, "career_history": [{"company": "CRED", "title": "AI Engineer", "start_date": "2025-02-01", "end_date": null, "duration_months": 16, "is_current": true, "industry": "Fintech", "company_size": "1001-5000", "description": "Implemented a RAG-based customer support chatbot integrated with our existing ticketing system. Built the document ingestion pipeline (chunking, embedding via OpenAI embeddings, storing in Pinecone) and the answer-generation layer (initially GPT-4, then a fine-tuned smaller model for cost control). Designed the evaluation framework with both automatic metrics (BLEU, ROUGE) and human-in-the-loop quality scores. Deployment cut average ticket resolution time by 31% for the supported categories."}, {"company": "Aganitha", "title": "Senior Data Scientist", "start_date": "2023-10-26", "end_date": "2025-01-18", "duration_months": 15, "is_current": false, "industry": "AI/ML", "company_size": "51-200", "description": "Developed a semantic search feature for an internal knowledge base of ~500K documents. Used sentence-transformers (all-MiniLM-L6-v2 initially, later upgraded to bge-base) with FAISS for fast nearest-neighbor retrieval. Designed the query expansion module that handles vocabulary mismatch between user queries and document terms. Reported search-relevance improvement of 35% over the prior Elasticsearch BM25 setup, validated through human relevance judgments."}, {"company": "Observe.AI", "title": "AI Engineer", "start_date": "2020-10-11", "end_date": "2023-10-26", "duration_months": 37, "is_current": false, "industry": "AI/ML", "company_size": "201-500", "description": "Built a content recommendation system serving 10M+ users that combined collaborative filtering with content-based ranking. The system uses item-item similarity (via sentence-transformer embeddings) for cold starts and a gradient-boosted model trained on engagement signals for warm users. Most of my time went into the feature pipeline (~200 features) and the A/B testing infrastructure. The launch improved 7-day retention by 6% and time spent per session by 14%."}, {"company": "Ola", "title": "Machine Learning Engineer", "start_date": "2019-10-17", "end_date": "2020-10-11", "duration_months": 12, "is_current": false, "industry": "Transportation", "company_size": "5001-10000", "description": "Owned the ranking layer for an e-commerce search product, evolving it from a hand-tuned scoring function to a learning-to-rank model over 9 months. Designed the relevance labeling pipeline (mix of click-through data and explicit human judgments), the feature pipeline, and the training/eval workflow. Most of the work was infrastructure and data quality \u2014 the modeling part was almost the easy bit. 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{"candidate_id": "CAND_0009691", "profile": {"anonymized_name": "Ira Subramanian", "headline": "Applied ML Engineer | ML, NLP, Recommendation Systems", "summary": "Machine learning engineer with 6.2 years of experience building ML-powered features in production. Strong background in NLP, recommendation systems, and applied AI; comfortable across the ML stack from feature engineering through deployment. Recently, I built our semantic search infrastructure from scratch \u2014 sentence-transformers, FAISS, the works. I care a lot about evaluation rigor \u2014 too many teams ship models without offline benchmarks they trust. My academic background is in CS/ML but my main learning has come from shipping real systems and seeing what holds up under production load. Open to senior IC roles in applied ML or AI engineering, ideally at product companies where I'd own a meaningful piece of the ML stack.", "location": "Indore, Madhya Pradesh", "country": "India", "years_of_experience": 6.2, "current_title": "Applied ML Engineer", "current_company": "LinkedIn", "current_company_size": "10001+", "current_industry": "Internet"}, "career_history": [{"company": "LinkedIn", "title": "Applied ML Engineer", "start_date": "2024-02-07", "end_date": null, "duration_months": 28, "is_current": true, "industry": "Internet", "company_size": "10001+", "description": "Built a content recommendation system serving 10M+ users that combined collaborative filtering with content-based ranking. The system uses item-item similarity (via sentence-transformer embeddings) for cold starts and a gradient-boosted model trained on engagement signals for warm users. Most of my time went into the feature pipeline (~200 features) and the A/B testing infrastructure. 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The launch improved 7-day retention by 6% and time spent per session by 14%."}, {"company": "Genpact AI", "title": "NLP Engineer", "start_date": "2020-04-28", "end_date": "2022-10-15", "duration_months": 30, "is_current": false, "industry": "AI Services", "company_size": "10001+", "description": "Built a content recommendation system serving 10M+ users that combined collaborative filtering with content-based ranking. The system uses item-item similarity (via sentence-transformer embeddings) for cold starts and a gradient-boosted model trained on engagement signals for warm users. Most of my time went into the feature pipeline (~200 features) and the A/B testing infrastructure. 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{"candidate_id": "CAND_0049896", "profile": {"anonymized_name": "Neha Vora", "headline": "Search Engineer | Search, Ranking & Retrieval", "summary": "Machine learning engineer with 7.3 years of experience building ML-powered features in production. Strong background in NLP, recommendation systems, and applied AI; comfortable across the ML stack from feature engineering through deployment. Recently, my main project for the last 18 months has been the recommendation system that powers our discovery feed. I've learned that most retrieval problems are actually evaluation problems in disguise. My academic background is in CS/ML but my main learning has come from shipping real systems and seeing what holds up under production load. 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Designed the evaluation framework with both automatic metrics (BLEU, ROUGE) and human-in-the-loop quality scores. Deployment cut average ticket resolution time by 31% for the supported categories."}, {"company": "Flipkart", "title": "Applied ML Engineer", "start_date": "2019-03-28", "end_date": "2022-09-08", "duration_months": 42, "is_current": false, "industry": "E-commerce", "company_size": "10001+", "description": "Built a content recommendation system serving 10M+ users that combined collaborative filtering with content-based ranking. The system uses item-item similarity (via sentence-transformer embeddings) for cold starts and a gradient-boosted model trained on engagement signals for warm users. Most of my time went into the feature pipeline (~200 features) and the A/B testing infrastructure. 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{"candidate_id": "CAND_0080534", "profile": {"anonymized_name": "Manish Iyer", "headline": "ML Engineer | Data Science & ML enthusiast", "summary": "Data scientist / ML engineer with 3.8 years of experience in applied machine learning. Worked across predictive modeling, NLP, analytics, and lightweight deployment workflows. Most of my recent work has been on predictive modeling for customer-facing problems \u2014 churn, conversion, lifetime value. I'm strongest at the modeling and analysis side; comfortable with Python, scikit-learn, pandas, and standard MLOps tooling, but I'm still building depth on the engineering and infra side of production ML. Looking for a role where I can step up to more end-to-end ownership of ML systems, not just modeling.", "location": "Vizag, Andhra Pradesh", "country": "India", "years_of_experience": 3.8, "current_title": "ML Engineer", "current_company": "Genpact AI", "current_company_size": "10001+", "current_industry": "AI Services"}, "career_history": [{"company": "Genpact AI", "title": "ML Engineer", "start_date": "2023-05-13", "end_date": null, "duration_months": 37, "is_current": true, "industry": "AI Services", "company_size": "10001+", "description": "Built recommendation-style features at a mid-stage startup \u2014 lighter weight than ranking systems at FAANG, but production. Used a combination of collaborative filtering (matrix factorization in implicit-feedback library) and gradient-boosted re-ranking over engagement signals. Pure ML side of the work; production deployment was handled by the platform team."}, {"company": "PhonePe", "title": "AI Specialist", "start_date": "2022-09-01", "end_date": "2023-04-29", "duration_months": 8, "is_current": false, "industry": "Fintech", "company_size": "5001-10000", "description": "Built recommendation-style features at a mid-stage startup \u2014 lighter weight than ranking systems at FAANG, but production. Used a combination of collaborative filtering (matrix factorization in implicit-feedback library) and gradient-boosted re-ranking over engagement signals. Pure ML side of the work; production deployment was handled by the platform team."}], "education": [{"institution": "Manipal Institute of Technology", "degree": "Ph.D", "field_of_study": "Machine Learning", "start_year": 2013, "end_year": 2018, "grade": "8.57 CGPA", "tier": "tier_2"}], "skills": [{"name": "Elasticsearch", "proficiency": "advanced", "endorsements": 54, "duration_months": 44}, {"name": "OpenSearch", "proficiency": "advanced", "endorsements": 30, "duration_months": 34}, {"name": "Airflow", "proficiency": "beginner", "endorsements": 5, "duration_months": 2}, {"name": "Kubeflow", "proficiency": "advanced", "endorsements": 12, "duration_months": 55}, {"name": "Fine-tuning LLMs", "proficiency": "intermediate", "endorsements": 8, "duration_months": 14}, {"name": "Haystack", "proficiency": "intermediate", "endorsements": 15, "duration_months": 27}, {"name": "OpenCV", "proficiency": "advanced", "endorsements": 54, "duration_months": 33}, {"name": "TensorFlow", "proficiency": "intermediate", "endorsements": 6, "duration_months": 19}, {"name": "PEFT", "proficiency": "intermediate", "endorsements": 5, "duration_months": 21}, {"name": "LangChain", "proficiency": "advanced", "endorsements": 49, "duration_months": 25}, {"name": "Weights & Biases", "proficiency": "intermediate", "endorsements": 4, "duration_months": 26}, {"name": "Reinforcement Learning", "proficiency": "intermediate", "endorsements": 2, "duration_months": 30}, {"name": "Deep Learning", "proficiency": "intermediate", "endorsements": 0, "duration_months": 16}, {"name": "Image Classification", "proficiency": "advanced", "endorsements": 24, "duration_months": 50}, {"name": "Node.js", "proficiency": "intermediate", "endorsements": 12, "duration_months": 24}, {"name": "Project Management", "proficiency": "intermediate", "endorsements": 0, "duration_months": 36}, {"name": "Feature Engineering", "proficiency": "intermediate", "endorsements": 2, "duration_months": 20}], "certifications": [], "languages": [{"language": "English", "proficiency": "professional"}, {"language": "Hindi", "proficiency": "native"}], "redrob_signals": {"profile_completeness_score": 95.7, "signup_date": "2026-05-14", "last_active_date": "2026-05-26", "open_to_work_flag": true, "profile_views_received_30d": 53, "applications_submitted_30d": 1, "recruiter_response_rate": 0.91, "avg_response_time_hours": 11.8, "skill_assessment_scores": {"Elasticsearch": 57.0, "OpenSearch": 65.2, "Kubeflow": 42.2, "OpenCV": 54.0}, "connection_count": 853, "endorsements_received": 107, "notice_period_days": 30, "expected_salary_range_inr_lpa": {"min": 29.0, "max": 45.2}, "preferred_work_mode": "flexible", "willing_to_relocate": false, "github_activity_score": 81.1, "search_appearance_30d": 339, "saved_by_recruiters_30d": 24, "interview_completion_rate": 0.89, "offer_acceptance_rate": -1, "verified_email": false, "verified_phone": true, "linkedin_connected": true}}
{"candidate_id": "CAND_0012837", "profile": {"anonymized_name": "Myra Chowdary", "headline": "Junior ML Engineer | Data Science & ML enthusiast", "summary": "Data scientist / ML engineer with 6.4 years of experience in applied machine learning. Worked across predictive modeling, NLP, analytics, and lightweight deployment workflows. My current role is split between dashboarding/analytics and shipping production ML models. I'm strongest at the modeling and analysis side; comfortable with Python, scikit-learn, pandas, and standard MLOps tooling, but I'm still building depth on the engineering and infra side of production ML. I'm looking to grow into a deeper AI/ML system-building role \u2014 closer to retrieval, LLMs, and modern ranking systems.", "location": "Chandigarh, Chandigarh", "country": "India", "years_of_experience": 6.4, "current_title": "Junior ML Engineer", "current_company": "Sarvam AI", "current_company_size": "51-200", "current_industry": "AI/ML"}, "career_history": [{"company": "Sarvam AI", "title": "Junior ML Engineer", "start_date": "2023-05-13", "end_date": null, "duration_months": 37, "is_current": true, "industry": "AI/ML", "company_size": "51-200", "description": "Worked on time-series forecasting models for supply-chain demand prediction at a logistics company. Built models in Prophet, LightGBM, and (for one project) a small LSTM \u2014 the LightGBM model ended up shipping. Also ran some reinforcement learning experiments for dynamic pricing but those didn't make it to production. The work was a mix of modeling, analysis, and stakeholder communication with the operations team."}, {"company": "CRED", "title": "ML Engineer", "start_date": "2021-03-10", "end_date": "2023-04-29", "duration_months": 26, "is_current": false, "industry": "Fintech", "company_size": "1001-5000", "description": "Built recommendation-style features at a mid-stage startup \u2014 lighter weight than ranking systems at FAANG, but production. Used a combination of collaborative filtering (matrix factorization in implicit-feedback library) and gradient-boosted re-ranking over engagement signals. Pure ML side of the work; production deployment was handled by the platform team."}, {"company": "CRED", "title": "Data Scientist", "start_date": "2020-01-31", "end_date": "2021-02-24", "duration_months": 13, "is_current": false, "industry": "Fintech", "company_size": "1001-5000", "description": "Built computer vision models for our product's image moderation feature using PyTorch \u2014 fine-tuned ResNet variants on a labeled dataset of ~200K images. Set up the training pipeline (data loading, augmentation, evaluation) and the inference service. Most of my project work has been in CV; I'm now interested in transitioning toward NLP/LLM work but my professional experience there is limited."}], "education": [{"institution": "Anna University", "degree": "Ph.D", "field_of_study": "Computer Engineering", "start_year": 2008, "end_year": 2013, "grade": "9.10 CGPA", "tier": "tier_2"}, {"institution": "Amity University", "degree": "B.Sc", "field_of_study": "Computer Science", "start_year": 2007, "end_year": 2011, "grade": "71%", "tier": "tier_3"}], "skills": [{"name": "Forecasting", "proficiency": "intermediate", "endorsements": 2, "duration_months": 29}, {"name": "Vector Search", "proficiency": "advanced", "endorsements": 10, "duration_months": 45}, {"name": "MLOps", "proficiency": "advanced", "endorsements": 58, "duration_months": 41}, {"name": "Weaviate", "proficiency": "advanced", "endorsements": 27, "duration_months": 46}, {"name": "LLMs", "proficiency": "intermediate", "endorsements": 15, "duration_months": 31}, {"name": "Semantic Search", "proficiency": "intermediate", "endorsements": 2, "duration_months": 26}, {"name": "Object Detection", "proficiency": "advanced", "endorsements": 41, "duration_months": 58}, {"name": "TypeScript", "proficiency": "intermediate", "endorsements": 10, "duration_months": 8}, {"name": "NLP", "proficiency": "advanced", "endorsements": 22, "duration_months": 24}, {"name": "Speech Recognition", "proficiency": "advanced", "endorsements": 29, "duration_months": 47}], "certifications": [], "languages": [{"language": "English", "proficiency": "professional"}, {"language": "Hindi", "proficiency": "conversational"}], "redrob_signals": {"profile_completeness_score": 65.5, "signup_date": "2026-03-18", "last_active_date": "2026-03-22", "open_to_work_flag": true, "profile_views_received_30d": 163, "applications_submitted_30d": 11, "recruiter_response_rate": 0.81, "avg_response_time_hours": 112.1, "skill_assessment_scores": {"Vector Search": 66.5, "MLOps": 59.7, "Weaviate": 40.8}, "connection_count": 136, "endorsements_received": 10, "notice_period_days": 60, "expected_salary_range_inr_lpa": {"min": 29.4, "max": 36.2}, "preferred_work_mode": "hybrid", "willing_to_relocate": true, "github_activity_score": -1, "search_appearance_30d": 375, "saved_by_recruiters_30d": 37, "interview_completion_rate": 0.52, "offer_acceptance_rate": -1, "verified_email": true, "verified_phone": true, "linkedin_connected": true}}
{"candidate_id": "CAND_0061339", "profile": {"anonymized_name": "Tanvi Dutta", "headline": "Search Engineer | Applied ML | Building intelligent products", "summary": "Machine learning engineer with 4.2 years of experience building ML-powered features in production. Strong background in NLP, recommendation systems, and applied AI; comfortable across the ML stack from feature engineering through deployment. Recently, I led the team that migrated our keyword-search-based product to embedding-based retrieval. I've learned that most retrieval problems are actually evaluation problems in disguise. My academic background is in CS/ML but my main learning has come from shipping real systems and seeing what holds up under production load. Open to senior IC roles in applied ML or AI engineering, ideally at product companies where I'd own a meaningful piece of the ML stack.", "location": "Chandigarh, Chandigarh", "country": "India", "years_of_experience": 4.2, "current_title": "Search Engineer", "current_company": "Rephrase.ai", "current_company_size": "11-50", "current_industry": "AI/ML"}, "career_history": [{"company": "Rephrase.ai", "title": "Search Engineer", "start_date": "2024-02-07", "end_date": null, "duration_months": 28, "is_current": true, "industry": "AI/ML", "company_size": "11-50", "description": "Built a content recommendation system serving 10M+ users that combined collaborative filtering with content-based ranking. The system uses item-item similarity (via sentence-transformer embeddings) for cold starts and a gradient-boosted model trained on engagement signals for warm users. Most of my time went into the feature pipeline (~200 features) and the A/B testing infrastructure. The launch improved 7-day retention by 6% and time spent per session by 14%."}, {"company": "Zoho", "title": "Senior Data Scientist", "start_date": "2023-01-06", "end_date": "2024-01-31", "duration_months": 13, "is_current": false, "industry": "SaaS", "company_size": "10001+", "description": "Trained and shipped multiple ranking models for our product's discovery feed using XGBoost and LightGBM. Designed features across three families: content metadata, user behavior signals, and item engagement history. Owned the offline-online correlation analysis that determined which offline metrics actually predicted A/B test outcomes. Worked closely with PMs to define the optimization target (click-through vs. dwell time vs. downstream conversion) \u2014 that work was as important as the modeling itself."}, {"company": "Krutrim", "title": "Applied ML Engineer", "start_date": "2022-04-27", "end_date": "2022-12-23", "duration_months": 8, "is_current": false, "industry": "AI/ML", "company_size": "201-500", "description": "Trained and shipped multiple ranking models for our product's discovery feed using XGBoost and LightGBM. Designed features across three families: content metadata, user behavior signals, and item engagement history. Owned the offline-online correlation analysis that determined which offline metrics actually predicted A/B test outcomes. Worked closely with PMs to define the optimization target (click-through vs. dwell time vs. downstream conversion) \u2014 that work was as important as the modeling itself."}], "education": [{"institution": "VJTI Mumbai", "degree": "B.Sc", "field_of_study": "Data Science", "start_year": 2006, "end_year": 2010, "grade": "7.58 CGPA", "tier": "tier_2"}], "skills": [{"name": "Information Retrieval", "proficiency": "expert", "endorsements": 26, "duration_months": 95}, {"name": "YOLO", "proficiency": "advanced", "endorsements": 0, "duration_months": 48}, {"name": "Data Pipelines", "proficiency": "beginner", "endorsements": 12, "duration_months": 5}, {"name": "Milvus", "proficiency": "expert", "endorsements": 2, "duration_months": 70}, {"name": "BentoML", "proficiency": "advanced", "endorsements": 18, "duration_months": 21}, {"name": "TensorFlow", "proficiency": "advanced", "endorsements": 29, "duration_months": 22}, {"name": "Vector Search", "proficiency": "expert", "endorsements": 30, "duration_months": 48}, {"name": "LlamaIndex", "proficiency": "expert", "endorsements": 55, "duration_months": 95}, {"name": "OpenSearch", "proficiency": "advanced", "endorsements": 9, "duration_months": 28}, {"name": "MLflow", "proficiency": "advanced", "endorsements": 33, "duration_months": 21}, {"name": "LLMs", "proficiency": "expert", "endorsements": 5, "duration_months": 40}, {"name": "Diffusion Models", "proficiency": "advanced", "endorsements": 9, "duration_months": 58}, {"name": "Prompt Engineering", "proficiency": "expert", "endorsements": 37, "duration_months": 58}, {"name": "TTS", "proficiency": "intermediate", "endorsements": 0, "duration_months": 23}], "certifications": [], "languages": [{"language": "English", "proficiency": "professional"}, {"language": "Hindi", "proficiency": "conversational"}], "redrob_signals": {"profile_completeness_score": 82.8, "signup_date": "2026-05-01", "last_active_date": "2026-04-06", "open_to_work_flag": true, "profile_views_received_30d": 232, "applications_submitted_30d": 8, "recruiter_response_rate": 0.9, "avg_response_time_hours": 63.1, "skill_assessment_scores": {"Information Retrieval": 51.8}, "connection_count": 1447, "endorsements_received": 67, "notice_period_days": 90, "expected_salary_range_inr_lpa": {"min": 33.9, "max": 39.3}, "preferred_work_mode": "remote", "willing_to_relocate": true, "github_activity_score": 85.7, "search_appearance_30d": 1023, "saved_by_recruiters_30d": 9, "interview_completion_rate": 0.65, "offer_acceptance_rate": 0.38, "verified_email": true, "verified_phone": true, "linkedin_connected": true}}
{"candidate_id": "CAND_0008239", "profile": {"anonymized_name": "Advik Iyer", "headline": "AI Engineer | Applied ML | Building intelligent products", "summary": "Machine learning engineer with 4.0 years of experience building ML-powered features in production. Strong background in NLP, recommendation systems, and applied AI; comfortable across the ML stack from feature engineering through deployment. Recently, I've been the de-facto ML lead on a small team, shipping models across NLP and recsys. I've spent enough time debugging production ranking issues to know which signals matter and which are noise. My academic background is in CS/ML but my main learning has come from shipping real systems and seeing what holds up under production load. Open to senior IC roles in applied ML or AI engineering, ideally at product companies where I'd own a meaningful piece of the ML stack.", "location": "Jaipur, Rajasthan", "country": "India", "years_of_experience": 4.0, "current_title": "AI Engineer", "current_company": "Apple", "current_company_size": "10001+", "current_industry": "Consumer Electronics"}, "career_history": [{"company": "Apple", "title": "AI Engineer", "start_date": "2022-06-17", "end_date": null, "duration_months": 48, "is_current": true, "industry": "Consumer Electronics", "company_size": "10001+", "description": "Owned the ranking layer for an e-commerce search product, evolving it from a hand-tuned scoring function to a learning-to-rank model over 9 months. Designed the relevance labeling pipeline (mix of click-through data and explicit human judgments), the feature pipeline, and the training/eval workflow. Most of the work was infrastructure and data quality \u2014 the modeling part was almost the easy bit. Final model improved revenue-per-search by 12%."}], "education": [{"institution": "IIT Hyderabad", "degree": "M.E.", "field_of_study": "Computer Science", "start_year": 2001, "end_year": 2006, "grade": "7.31 CGPA", "tier": "tier_1"}], "skills": [{"name": "Milvus", "proficiency": "expert", "endorsements": 54, "duration_months": 72}, {"name": "Computer Vision", "proficiency": "intermediate", "endorsements": 0, "duration_months": 10}, {"name": "Feature Engineering", "proficiency": "intermediate", "endorsements": 4, "duration_months": 27}, {"name": "Semantic Search", "proficiency": "advanced", "endorsements": 37, "duration_months": 50}, {"name": "Diffusion Models", "proficiency": "advanced", "endorsements": 5, "duration_months": 37}, {"name": "Elasticsearch", "proficiency": "advanced", "endorsements": 59, "duration_months": 39}, {"name": "LangChain", "proficiency": "expert", "endorsements": 33, "duration_months": 61}, {"name": "Weaviate", "proficiency": "advanced", "endorsements": 47, "duration_months": 33}, {"name": "Forecasting", "proficiency": "advanced", "endorsements": 0, "duration_months": 25}, {"name": "Recommendation Systems", "proficiency": "advanced", "endorsements": 1, "duration_months": 31}, {"name": "Vector Search", "proficiency": "expert", "endorsements": 32, "duration_months": 61}, {"name": "MLflow", "proficiency": "advanced", "endorsements": 16, "duration_months": 18}, {"name": "Python", "proficiency": "advanced", "endorsements": 57, "duration_months": 39}, {"name": "BentoML", "proficiency": "advanced", "endorsements": 54, "duration_months": 33}, {"name": "PyTorch", "proficiency": "expert", "endorsements": 16, "duration_months": 56}, {"name": "FAISS", "proficiency": "expert", "endorsements": 25, "duration_months": 44}, {"name": "scikit-learn", "proficiency": "advanced", "endorsements": 46, "duration_months": 49}, {"name": "PEFT", "proficiency": "advanced", "endorsements": 3, "duration_months": 27}], "certifications": [], "languages": [{"language": "English", "proficiency": "professional"}, {"language": "Hindi", "proficiency": "native"}], "redrob_signals": {"profile_completeness_score": 96.4, "signup_date": "2025-07-13", "last_active_date": "2026-03-15", "open_to_work_flag": true, "profile_views_received_30d": 23, "applications_submitted_30d": 3, "recruiter_response_rate": 0.73, "avg_response_time_hours": 51.0, "skill_assessment_scores": {}, "connection_count": 194, "endorsements_received": 68, "notice_period_days": 15, "expected_salary_range_inr_lpa": {"min": 31.8, "max": 55.1}, "preferred_work_mode": "remote", "willing_to_relocate": true, "github_activity_score": 56.6, "search_appearance_30d": 763, "saved_by_recruiters_30d": 56, "interview_completion_rate": 0.55, "offer_acceptance_rate": 0.44, "verified_email": true, "verified_phone": false, "linkedin_connected": true}}
{"candidate_id": "CAND_0005509", "profile": {"anonymized_name": "Arnav Khanna", "headline": "Data Scientist | 6.0 yrs in analytics & ML", "summary": "Data scientist / ML engineer with 6.0 years of experience in applied machine learning. Worked across predictive modeling, NLP, analytics, and lightweight deployment workflows. I've spent the last couple of years building NLP-based classification and information extraction pipelines. I'm strongest at the modeling and analysis side; comfortable with Python, scikit-learn, pandas, and standard MLOps tooling, but I'm still building depth on the engineering and infra side of production ML. Looking for a role where I can step up to more end-to-end ownership of ML systems, not just modeling.", "location": "Noida, Uttar Pradesh", "country": "India", "years_of_experience": 6.0, "current_title": "Data Scientist", "current_company": "Ola", "current_company_size": "5001-10000", "current_industry": "Transportation"}, "career_history": [{"company": "Ola", "title": "Data Scientist", "start_date": "2024-02-07", "end_date": null, "duration_months": 28, "is_current": true, "industry": "Transportation", "company_size": "5001-10000", "description": "Built recommendation-style features at a mid-stage startup \u2014 lighter weight than ranking systems at FAANG, but production. Used a combination of collaborative filtering (matrix factorization in implicit-feedback library) and gradient-boosted re-ranking over engagement signals. Pure ML side of the work; production deployment was handled by the platform team."}, {"company": "Haptik", "title": "AI Research Engineer", "start_date": "2022-10-31", "end_date": "2024-01-24", "duration_months": 15, "is_current": false, "industry": "Conversational AI", "company_size": "201-500", "description": "Built computer vision models for our product's image moderation feature using PyTorch \u2014 fine-tuned ResNet variants on a labeled dataset of ~200K images. Set up the training pipeline (data loading, augmentation, evaluation) and the inference service. Most of my project work has been in CV; I'm now interested in transitioning toward NLP/LLM work but my professional experience there is limited."}, {"company": "Sarvam AI", "title": "ML Engineer", "start_date": "2020-08-12", "end_date": "2022-10-31", "duration_months": 27, "is_current": false, "industry": "AI/ML", "company_size": "51-200", "description": "Built recommendation-style features at a mid-stage startup \u2014 lighter weight than ranking systems at FAANG, but production. Used a combination of collaborative filtering (matrix factorization in implicit-feedback library) and gradient-boosted re-ranking over engagement signals. Pure ML side of the work; production deployment was handled by the platform team."}], "education": [{"institution": "Chandigarh University", "degree": "B.E.", "field_of_study": "Artificial Intelligence", "start_year": 2012, "end_year": 2017, "grade": "7.25 CGPA", "tier": "tier_3"}], "skills": [{"name": "Diffusion Models", "proficiency": "intermediate", "endorsements": 2, "duration_months": 12}, {"name": "GANs", "proficiency": "advanced", "endorsements": 28, "duration_months": 21}, {"name": "JavaScript", "proficiency": "intermediate", "endorsements": 9, "duration_months": 11}, {"name": "Python", "proficiency": "intermediate", "endorsements": 10, "duration_months": 33}, {"name": "Fine-tuning LLMs", "proficiency": "intermediate", "endorsements": 12, "duration_months": 13}, {"name": "dbt", "proficiency": "intermediate", "endorsements": 3, "duration_months": 31}, {"name": "gRPC", "proficiency": "beginner", "endorsements": 11, "duration_months": 13}, {"name": "Object Detection", "proficiency": "intermediate", "endorsements": 2, "duration_months": 33}, {"name": "Feature Engineering", "proficiency": "advanced", "endorsements": 11, "duration_months": 44}, {"name": "LlamaIndex", "proficiency": "intermediate", "endorsements": 3, "duration_months": 25}, {"name": "Reinforcement Learning", "proficiency": "intermediate", "endorsements": 3, "duration_months": 27}, {"name": "Qdrant", "proficiency": "advanced", "endorsements": 38, "duration_months": 23}, {"name": "Weights & Biases", "proficiency": "advanced", "endorsements": 20, "duration_months": 48}, {"name": "Image Classification", "proficiency": "intermediate", "endorsements": 5, "duration_months": 18}], "certifications": [{"name": "NLP Specialization", "issuer": "Coursera/DeepLearning.AI", "year": 2018}, {"name": "LangChain for LLM Application Development", "issuer": "DeepLearning.AI", "year": 2022}], "languages": [{"language": "English", "proficiency": "professional"}, {"language": "Hindi", "proficiency": "professional"}], "redrob_signals": {"profile_completeness_score": 80.7, "signup_date": "2024-08-18", "last_active_date": "2026-05-25", "open_to_work_flag": true, "profile_views_received_30d": 117, "applications_submitted_30d": 17, "recruiter_response_rate": 0.4, "avg_response_time_hours": 35.6, "skill_assessment_scores": {}, "connection_count": 388, "endorsements_received": 105, "notice_period_days": 30, "expected_salary_range_inr_lpa": {"min": 24.0, "max": 52.9}, "preferred_work_mode": "remote", "willing_to_relocate": true, "github_activity_score": 74.2, "search_appearance_30d": 732, "saved_by_recruiters_30d": 29, "interview_completion_rate": 0.61, "offer_acceptance_rate": 0.85, "verified_email": true, "verified_phone": true, "linkedin_connected": false}}
{"candidate_id": "CAND_0031593", "profile": {"anonymized_name": "Aanya Chopra", "headline": "Search Engineer | ML, NLP, Recommendation Systems", "summary": "Machine learning engineer with 7.8 years of experience building ML-powered features in production. Strong background in NLP, recommendation systems, and applied AI; comfortable across the ML stack from feature engineering through deployment. Recently, my main project for the last 18 months has been the recommendation system that powers our discovery feed. I've learned that most retrieval problems are actually evaluation problems in disguise. My academic background is in CS/ML but my main learning has come from shipping real systems and seeing what holds up under production load. Open to senior IC roles in applied ML or AI engineering, ideally at product companies where I'd own a meaningful piece of the ML stack.", "location": "Kolkata, West Bengal", "country": "India", "years_of_experience": 7.8, "current_title": "Search Engineer", "current_company": "Genpact AI", "current_company_size": "10001+", "current_industry": "AI Services"}, "career_history": [{"company": "Genpact AI", "title": "Search Engineer", "start_date": "2023-04-13", "end_date": null, "duration_months": 38, "is_current": true, "industry": "AI Services", "company_size": "10001+", "description": "Owned the ranking layer for an e-commerce search product, evolving it from a hand-tuned scoring function to a learning-to-rank model over 9 months. Designed the relevance labeling pipeline (mix of click-through data and explicit human judgments), the feature pipeline, and the training/eval workflow. Most of the work was infrastructure and data quality \u2014 the modeling part was almost the easy bit. Final model improved revenue-per-search by 12%."}, {"company": "Unacademy", "title": "NLP Engineer", "start_date": "2021-07-22", "end_date": "2023-04-13", "duration_months": 21, "is_current": false, "industry": "EdTech", "company_size": "5001-10000", "description": "Implemented a RAG-based customer support chatbot integrated with our existing ticketing system. Built the document ingestion pipeline (chunking, embedding via OpenAI embeddings, storing in Pinecone) and the answer-generation layer (initially GPT-4, then a fine-tuned smaller model for cost control). Designed the evaluation framework with both automatic metrics (BLEU, ROUGE) and human-in-the-loop quality scores. Deployment cut average ticket resolution time by 31% for the supported categories."}, {"company": "Aganitha", "title": "Search Engineer", "start_date": "2018-11-05", "end_date": "2021-07-22", "duration_months": 33, "is_current": false, "industry": "AI/ML", "company_size": "51-200", "description": "Built a content recommendation system serving 10M+ users that combined collaborative filtering with content-based ranking. The system uses item-item similarity (via sentence-transformer embeddings) for cold starts and a gradient-boosted model trained on engagement signals for warm users. Most of my time went into the feature pipeline (~200 features) and the A/B testing infrastructure. The launch improved 7-day retention by 6% and time spent per session by 14%."}], "education": [{"institution": "SRM University", "degree": "Ph.D", "field_of_study": "Information Technology", "start_year": 2016, "end_year": 2019, "grade": "78%", "tier": "tier_2"}], "skills": [{"name": "Haystack", "proficiency": "expert", "endorsements": 30, "duration_months": 82}, {"name": "OpenCV", "proficiency": "advanced", "endorsements": 58, "duration_months": 52}, {"name": "Hugging Face Transformers", "proficiency": "advanced", "endorsements": 57, "duration_months": 32}, {"name": "CNN", "proficiency": "advanced", "endorsements": 44, "duration_months": 31}, {"name": "Java", "proficiency": "beginner", "endorsements": 7, "duration_months": 10}, {"name": "ASR", "proficiency": "advanced", "endorsements": 36, "duration_months": 52}, {"name": "BentoML", "proficiency": "advanced", "endorsements": 53, "duration_months": 51}, {"name": "Recommendation Systems", "proficiency": "advanced", "endorsements": 42, "duration_months": 18}, {"name": "HTML", "proficiency": "intermediate", "endorsements": 3, "duration_months": 31}, {"name": "Statistical Modeling", "proficiency": "intermediate", "endorsements": 0, "duration_months": 24}, {"name": "GCP", "proficiency": "intermediate", "endorsements": 14, "duration_months": 31}, {"name": "Weights & Biases", "proficiency": "intermediate", "endorsements": 15, "duration_months": 16}, {"name": "BM25", "proficiency": "expert", "endorsements": 46, "duration_months": 63}, {"name": "LangChain", "proficiency": "expert", "endorsements": 11, "duration_months": 73}, {"name": "Embeddings", "proficiency": "expert", "endorsements": 41, "duration_months": 94}, {"name": "Elasticsearch", "proficiency": "expert", "endorsements": 57, "duration_months": 58}], "certifications": [], "languages": [{"language": "English", "proficiency": "native"}, {"language": "Hindi", "proficiency": "native"}], "redrob_signals": {"profile_completeness_score": 50.1, "signup_date": "2025-01-23", "last_active_date": "2026-03-19", "open_to_work_flag": true, "profile_views_received_30d": 235, "applications_submitted_30d": 20, "recruiter_response_rate": 0.58, "avg_response_time_hours": 6.9, "skill_assessment_scores": {"Haystack": 63.8, "OpenCV": 53.6}, "connection_count": 681, "endorsements_received": 55, "notice_period_days": 90, "expected_salary_range_inr_lpa": {"min": 23.8, "max": 41.9}, "preferred_work_mode": "remote", "willing_to_relocate": true, "github_activity_score": 23.2, "search_appearance_30d": 900, "saved_by_recruiters_30d": 17, "interview_completion_rate": 0.79, "offer_acceptance_rate": 0.83, "verified_email": true, "verified_phone": true, "linkedin_connected": true}}
{"candidate_id": "CAND_0042100", "profile": {"anonymized_name": "Siya Trivedi", "headline": "Machine Learning Engineer | Applied ML | Building intelligent products", "summary": "Machine learning engineer with 7.3 years of experience building ML-powered features in production. Strong background in NLP, recommendation systems, and applied AI; comfortable across the ML stack from feature engineering through deployment. Recently, I shipped our first RAG-based feature this year and now own the eval framework for it. Along the way I've gotten comfortable with the operational side \u2014 A/B testing, drift monitoring, retraining schedules. My academic background is in CS/ML but my main learning has come from shipping real systems and seeing what holds up under production load. Open to senior IC roles in applied ML or AI engineering, ideally at product companies where I'd own a meaningful piece of the ML stack.", "location": "Singapore", "country": "Singapore", "years_of_experience": 7.3, "current_title": "Machine Learning Engineer", "current_company": "Freshworks", "current_company_size": "5001-10000", "current_industry": "SaaS"}, "career_history": [{"company": "Freshworks", "title": "Machine Learning Engineer", "start_date": "2022-02-17", "end_date": null, "duration_months": 52, "is_current": true, "industry": "SaaS", "company_size": "5001-10000", "description": "Owned the ranking layer for an e-commerce search product, evolving it from a hand-tuned scoring function to a learning-to-rank model over 9 months. Designed the relevance labeling pipeline (mix of click-through data and explicit human judgments), the feature pipeline, and the training/eval workflow. Most of the work was infrastructure and data quality \u2014 the modeling part was almost the easy bit. Final model improved revenue-per-search by 12%."}, {"company": "Netflix", "title": "Senior Data Scientist", "start_date": "2019-04-20", "end_date": "2022-02-03", "duration_months": 34, "is_current": false, "industry": "Media", "company_size": "10001+", "description": "Trained and shipped multiple ranking models for our product's discovery feed using XGBoost and LightGBM. Designed features across three families: content metadata, user behavior signals, and item engagement history. Owned the offline-online correlation analysis that determined which offline metrics actually predicted A/B test outcomes. Worked closely with PMs to define the optimization target (click-through vs. dwell time vs. downstream conversion) \u2014 that work was as important as the modeling itself."}], "education": [{"institution": "VJTI Mumbai", "degree": "B.Tech", "field_of_study": "Information Technology", "start_year": 2003, "end_year": 2006, "grade": "8.92 CGPA", "tier": "tier_2"}], "skills": [{"name": "Elasticsearch", "proficiency": "expert", "endorsements": 26, "duration_months": 65}, {"name": "Statistical Modeling", "proficiency": "advanced", "endorsements": 55, "duration_months": 30}, {"name": "Learning to Rank", "proficiency": "expert", "endorsements": 30, "duration_months": 36}, {"name": "Recommendation Systems", "proficiency": "expert", "endorsements": 24, "duration_months": 85}, {"name": "Semantic Search", "proficiency": "advanced", "endorsements": 43, "duration_months": 48}, {"name": "Fine-tuning LLMs", "proficiency": "expert", "endorsements": 0, "duration_months": 78}, {"name": "Image Classification", "proficiency": "intermediate", "endorsements": 13, "duration_months": 22}, {"name": "Pinecone", "proficiency": "expert", "endorsements": 20, "duration_months": 69}, {"name": "LLMs", "proficiency": "expert", "endorsements": 33, "duration_months": 61}, {"name": "QLoRA", "proficiency": "expert", "endorsements": 37, "duration_months": 84}, {"name": "Reinforcement Learning", "proficiency": "advanced", "endorsements": 4, "duration_months": 60}, {"name": "PyTorch", "proficiency": "advanced", "endorsements": 25, "duration_months": 35}, {"name": "Next.js", "proficiency": "beginner", "endorsements": 2, "duration_months": 9}], "certifications": [], "languages": [{"language": "English", "proficiency": "professional"}, {"language": "Hindi", "proficiency": "native"}], "redrob_signals": {"profile_completeness_score": 96.7, "signup_date": "2025-10-03", "last_active_date": "2026-03-23", "open_to_work_flag": true, "profile_views_received_30d": 138, "applications_submitted_30d": 18, "recruiter_response_rate": 0.87, "avg_response_time_hours": 16.2, "skill_assessment_scores": {"Elasticsearch": 68.7, "Statistical Modeling": 81.5}, "connection_count": 1037, "endorsements_received": 32, "notice_period_days": 90, "expected_salary_range_inr_lpa": {"min": 36.4, "max": 41.9}, "preferred_work_mode": "flexible", "willing_to_relocate": true, "github_activity_score": 43.3, "search_appearance_30d": 462, "saved_by_recruiters_30d": 11, "interview_completion_rate": 0.67, "offer_acceptance_rate": -1, "verified_email": true, "verified_phone": true, "linkedin_connected": true}}
{"candidate_id": "CAND_0090155", "profile": {"anonymized_name": "Sai Chowdary", "headline": "ML Engineer | 5.8 yrs in analytics & ML", "summary": "Data scientist / ML engineer with 5.8 years of experience in applied machine learning. Worked across predictive modeling, NLP, analytics, and lightweight deployment workflows. I've been working on recommendation-style features but lighter on the deep-learning side \u2014 mostly classical methods like collaborative filtering and gradient-boosted models. I'm strongest at the modeling and analysis side; comfortable with Python, scikit-learn, pandas, and standard MLOps tooling, but I'm still building depth on the engineering and infra side of production ML. I'm looking to grow into a deeper AI/ML system-building role \u2014 closer to retrieval, LLMs, and modern ranking systems.", "location": "Pune, Maharashtra", "country": "India", "years_of_experience": 5.8, "current_title": "ML Engineer", "current_company": "Swiggy", "current_company_size": "5001-10000", "current_industry": "Food Delivery"}, "career_history": [{"company": "Swiggy", "title": "ML Engineer", "start_date": "2024-12-03", "end_date": null, "duration_months": 18, "is_current": true, "industry": "Food Delivery", "company_size": "5001-10000", "description": "Built NLP pipelines for sentiment analysis and document classification \u2014 primarily for an internal feedback-analytics dashboard. Started with sklearn-based bag-of-words models, then moved to transformer-based classifiers (DistilBERT) for the harder classes. Comfortable with PyTorch and Hugging Face but most of my training experience has been on small datasets and pre-trained model fine-tuning, not from-scratch model design."}, {"company": "InMobi", "title": "AI Specialist", "start_date": "2022-11-30", "end_date": "2024-11-19", "duration_months": 24, "is_current": false, "industry": "AdTech", "company_size": "1001-5000", "description": "Worked on customer-facing predictive modeling for an e-commerce platform \u2014 churn prediction, conversion likelihood, lifetime value estimation. Used scikit-learn and XGBoost; main models were gradient-boosted trees with ~80 hand-engineered features. The work was split roughly 60/40 between modeling and data prep / SQL. The churn model is now used by the retention team, though my role was more on the modeling side than the productionization."}, {"company": "upGrad", "title": "AI Research Engineer", "start_date": "2020-09-04", "end_date": "2022-11-23", "duration_months": 27, "is_current": false, "industry": "EdTech", "company_size": "1001-5000", "description": "Worked on customer-facing predictive modeling for an e-commerce platform \u2014 churn prediction, conversion likelihood, lifetime value estimation. Used scikit-learn and XGBoost; main models were gradient-boosted trees with ~80 hand-engineered features. The work was split roughly 60/40 between modeling and data prep / SQL. The churn model is now used by the retention team, though my role was more on the modeling side than the productionization."}], "education": [{"institution": "Anna University", "degree": "M.E.", "field_of_study": "Information Technology", "start_year": 2007, "end_year": 2010, "grade": "7.55 CGPA", "tier": "tier_2"}], "skills": [{"name": "OpenCV", "proficiency": "advanced", "endorsements": 27, "duration_months": 43}, {"name": "Weights & Biases", "proficiency": "intermediate", "endorsements": 13, "duration_months": 36}, {"name": "GANs", "proficiency": "advanced", "endorsements": 33, "duration_months": 54}, {"name": "Milvus", "proficiency": "intermediate", "endorsements": 9, "duration_months": 26}, {"name": "CNN", "proficiency": "advanced", "endorsements": 55, "duration_months": 48}, {"name": "Redux", "proficiency": "intermediate", "endorsements": 9, "duration_months": 15}, {"name": "Statistical Modeling", "proficiency": "intermediate", "endorsements": 2, "duration_months": 30}, {"name": "Qdrant", "proficiency": "intermediate", "endorsements": 15, "duration_months": 18}, {"name": "Pinecone", "proficiency": "advanced", "endorsements": 46, "duration_months": 52}, {"name": "PEFT", "proficiency": "intermediate", "endorsements": 11, "duration_months": 35}, {"name": "Diffusion Models", "proficiency": "intermediate", "endorsements": 15, "duration_months": 9}, {"name": "QLoRA", "proficiency": "intermediate", "endorsements": 11, "duration_months": 12}, {"name": "Feature Engineering", "proficiency": "intermediate", "endorsements": 8, "duration_months": 22}], "certifications": [], "languages": [{"language": "English", "proficiency": "professional"}, {"language": "Hindi", "proficiency": "professional"}], "redrob_signals": {"profile_completeness_score": 58.3, "signup_date": "2025-09-04", "last_active_date": "2026-05-13", "open_to_work_flag": true, "profile_views_received_30d": 201, "applications_submitted_30d": 12, "recruiter_response_rate": 0.64, "avg_response_time_hours": 18.5, "skill_assessment_scores": {"OpenCV": 41.3, "GANs": 54.7, "CNN": 81.3, "Pinecone": 79.8}, "connection_count": 69, "endorsements_received": 80, "notice_period_days": 90, "expected_salary_range_inr_lpa": {"min": 24.7, "max": 35.3}, "preferred_work_mode": "onsite", "willing_to_relocate": false, "github_activity_score": 75.9, "search_appearance_30d": 751, "saved_by_recruiters_30d": 27, "interview_completion_rate": 0.88, "offer_acceptance_rate": -1, "verified_email": true, "verified_phone": true, "linkedin_connected": true}}
{"candidate_id": "CAND_0016659", "profile": {"anonymized_name": "Priya Dalal", "headline": "ML Engineer | Data Science & ML enthusiast", "summary": "Data scientist / ML engineer with 4.4 years of experience in applied machine learning. Worked across predictive modeling, NLP, analytics, and lightweight deployment workflows. I've spent the last couple of years building NLP-based classification and information extraction pipelines. I'm strongest at the modeling and analysis side; comfortable with Python, scikit-learn, pandas, and standard MLOps tooling, but I'm still building depth on the engineering and infra side of production ML. I want to grow into senior AI engineering \u2014 get serious about LLMs and retrieval beyond the surface level.", "location": "Coimbatore, Tamil Nadu", "country": "India", "years_of_experience": 4.4, "current_title": "ML Engineer", "current_company": "Glance", "current_company_size": "501-1000", "current_industry": "AI/ML"}, "career_history": [{"company": "Glance", "title": "ML Engineer", "start_date": "2023-04-13", "end_date": null, "duration_months": 38, "is_current": true, "industry": "AI/ML", "company_size": "501-1000", "description": "Built NLP pipelines for sentiment analysis and document classification \u2014 primarily for an internal feedback-analytics dashboard. Started with sklearn-based bag-of-words models, then moved to transformer-based classifiers (DistilBERT) for the harder classes. Comfortable with PyTorch and Hugging Face but most of my training experience has been on small datasets and pre-trained model fine-tuning, not from-scratch model design."}, {"company": "Flipkart", "title": "ML Engineer", "start_date": "2022-02-17", "end_date": "2023-04-13", "duration_months": 14, "is_current": false, "industry": "E-commerce", "company_size": "10001+", "description": "Built recommendation-style features at a mid-stage startup \u2014 lighter weight than ranking systems at FAANG, but production. Used a combination of collaborative filtering (matrix factorization in implicit-feedback library) and gradient-boosted re-ranking over engagement signals. Pure ML side of the work; production deployment was handled by the platform team."}], "education": [{"institution": "IIT Hyderabad", "degree": "B.Tech", "field_of_study": "Artificial Intelligence", "start_year": 2018, "end_year": 2021, "grade": "7.90 CGPA", "tier": "tier_1"}], "skills": [{"name": "PowerPoint", "proficiency": "intermediate", "endorsements": 9, "duration_months": 17}, {"name": "TTS", "proficiency": "advanced", "endorsements": 0, "duration_months": 38}, {"name": "Sentence Transformers", "proficiency": "intermediate", "endorsements": 6, "duration_months": 22}, {"name": "Apache Beam", "proficiency": "intermediate", "endorsements": 14, "duration_months": 20}, {"name": "Pinecone", "proficiency": "advanced", "endorsements": 17, "duration_months": 31}, {"name": "PEFT", "proficiency": "intermediate", "endorsements": 12, "duration_months": 27}, {"name": "GraphQL", "proficiency": "intermediate", "endorsements": 7, "duration_months": 14}, {"name": "FAISS", "proficiency": "intermediate", "endorsements": 9, "duration_months": 32}, {"name": "Information Retrieval", "proficiency": "advanced", "endorsements": 43, "duration_months": 22}, {"name": "Weaviate", "proficiency": "advanced", "endorsements": 51, "duration_months": 43}, {"name": "Airflow", "proficiency": "intermediate", "endorsements": 13, "duration_months": 16}, {"name": "scikit-learn", "proficiency": "advanced", "endorsements": 59, "duration_months": 40}, {"name": "CNN", "proficiency": "advanced", "endorsements": 16, "duration_months": 33}, {"name": "OpenCV", "proficiency": "advanced", "endorsements": 2, "duration_months": 57}, {"name": "Feature Engineering", "proficiency": "intermediate", "endorsements": 1, "duration_months": 10}], "certifications": [{"name": "Google Cloud Professional ML Engineer", "issuer": "Google Cloud", "year": 2023}], "languages": [{"language": "English", "proficiency": "professional"}, {"language": "Hindi", "proficiency": "professional"}], "redrob_signals": {"profile_completeness_score": 98.3, "signup_date": "2024-06-21", "last_active_date": "2026-05-22", "open_to_work_flag": true, "profile_views_received_30d": 203, "applications_submitted_30d": 17, "recruiter_response_rate": 0.89, "avg_response_time_hours": 10.2, "skill_assessment_scores": {"TTS": 50.6, "Pinecone": 68.4, "Information Retrieval": 77.5, "Weaviate": 69.6}, "connection_count": 111, "endorsements_received": 66, "notice_period_days": 45, "expected_salary_range_inr_lpa": {"min": 31.8, "max": 54.5}, "preferred_work_mode": "flexible", "willing_to_relocate": false, "github_activity_score": 13.4, "search_appearance_30d": 46, "saved_by_recruiters_30d": 37, "interview_completion_rate": 0.9, "offer_acceptance_rate": 0.63, "verified_email": false, "verified_phone": false, "linkedin_connected": true}}
{"candidate_id": "CAND_0064256", "profile": {"anonymized_name": "Meera Gupta", "headline": "Junior ML Engineer | 6.4 yrs in analytics & ML", "summary": "Data scientist / ML engineer with 6.4 years of experience in applied machine learning. Worked across predictive modeling, NLP, analytics, and lightweight deployment workflows. Most of my recent work has been on predictive modeling for customer-facing problems \u2014 churn, conversion, lifetime value. I'm strongest at the modeling and analysis side; comfortable with Python, scikit-learn, pandas, and standard MLOps tooling, but I'm still building depth on the engineering and infra side of production ML. I'm looking to grow into a deeper AI/ML system-building role \u2014 closer to retrieval, LLMs, and modern ranking systems.", "location": "Kolkata, West Bengal", "country": "India", "years_of_experience": 6.4, "current_title": "Junior ML Engineer", "current_company": "BYJU'S", "current_company_size": "10001+", "current_industry": "EdTech"}, "career_history": [{"company": "BYJU'S", "title": "Junior ML Engineer", "start_date": "2022-11-14", "end_date": null, "duration_months": 43, "is_current": true, "industry": "EdTech", "company_size": "10001+", "description": "Built computer vision models for our product's image moderation feature using PyTorch \u2014 fine-tuned ResNet variants on a labeled dataset of ~200K images. Set up the training pipeline (data loading, augmentation, evaluation) and the inference service. Most of my project work has been in CV; I'm now interested in transitioning toward NLP/LLM work but my professional experience there is limited."}, {"company": "Aganitha", "title": "AI Research Engineer", "start_date": "2019-12-30", "end_date": "2022-09-15", "duration_months": 33, "is_current": false, "industry": "AI/ML", "company_size": "51-200", "description": "Contributed to ML feature engineering and model deployment for a fraud-detection product. My main role was engineering: building the Flask-based prediction API, integrating with the feature store, and writing the model-serving observability layer. I worked closely with senior data scientists but my own modeling work was secondary \u2014 I was the production-side engineer."}], "education": [{"institution": "Bharati Vidyapeeth", "degree": "M.S.", "field_of_study": "Mechanical Engineering", "start_year": 2001, "end_year": 2006, "grade": "9.15 CGPA", "tier": "tier_3"}, {"institution": "Symbiosis International", "degree": "M.E.", "field_of_study": "Chemical Engineering", "start_year": 2002, "end_year": 2005, "grade": "75%", "tier": "tier_3"}], "skills": [{"name": "Microservices", "proficiency": "beginner", "endorsements": 13, "duration_months": 8}, {"name": "PyTorch", "proficiency": "intermediate", "endorsements": 14, "duration_months": 13}, {"name": "MLOps", "proficiency": "intermediate", "endorsements": 2, "duration_months": 25}, {"name": "Python", "proficiency": "advanced", "endorsements": 43, "duration_months": 26}, {"name": "CNN", "proficiency": "intermediate", "endorsements": 8, "duration_months": 23}, {"name": "Diffusion Models", "proficiency": "advanced", "endorsements": 2, "duration_months": 41}, {"name": "Statistical Modeling", "proficiency": "intermediate", "endorsements": 15, "duration_months": 20}, {"name": "Object Detection", "proficiency": "advanced", "endorsements": 34, "duration_months": 44}, {"name": "Embeddings", "proficiency": "intermediate", "endorsements": 10, "duration_months": 29}, {"name": "OpenCV", "proficiency": "intermediate", "endorsements": 0, "duration_months": 14}, {"name": "Hugging Face Transformers", "proficiency": "advanced", "endorsements": 50, "duration_months": 40}, {"name": "BentoML", "proficiency": "advanced", "endorsements": 35, "duration_months": 42}, {"name": "REST APIs", "proficiency": "beginner", "endorsements": 8, "duration_months": 10}, {"name": "Feature Engineering", "proficiency": "intermediate", "endorsements": 4, "duration_months": 12}], "certifications": [], "languages": [{"language": "English", "proficiency": "professional"}, {"language": "Hindi", "proficiency": "native"}], "redrob_signals": {"profile_completeness_score": 99.0, "signup_date": "2025-12-23", "last_active_date": "2026-05-22", "open_to_work_flag": true, "profile_views_received_30d": 17, "applications_submitted_30d": 6, "recruiter_response_rate": 0.86, "avg_response_time_hours": 3.7, "skill_assessment_scores": {"Python": 53.6, "Diffusion Models": 62.4, "Object Detection": 58.7, "Hugging Face Transformers": 84.0}, "connection_count": 1065, "endorsements_received": 111, "notice_period_days": 30, "expected_salary_range_inr_lpa": {"min": 33.1, "max": 40.2}, "preferred_work_mode": "flexible", "willing_to_relocate": false, "github_activity_score": -1, "search_appearance_30d": 559, "saved_by_recruiters_30d": 18, "interview_completion_rate": 0.67, "offer_acceptance_rate": -1, "verified_email": true, "verified_phone": true, "linkedin_connected": true}}
{"candidate_id": "CAND_0061655", "profile": {"anonymized_name": "Mira Banerjee", "headline": "Machine Learning Engineer | ML, NLP, Recommendation Systems", "summary": "Machine learning engineer with 4.6 years of experience building ML-powered features in production. Strong background in NLP, recommendation systems, and applied AI; comfortable across the ML stack from feature engineering through deployment. Recently, my main project for the last 18 months has been the recommendation system that powers our discovery feed. I care a lot about evaluation rigor \u2014 too many teams ship models without offline benchmarks they trust. My academic background is in CS/ML but my main learning has come from shipping real systems and seeing what holds up under production load. Open to senior IC roles in applied ML or AI engineering, ideally at product companies where I'd own a meaningful piece of the ML stack.", "location": "Jaipur, Rajasthan", "country": "India", "years_of_experience": 4.6, "current_title": "Machine Learning Engineer", "current_company": "Krutrim", "current_company_size": "201-500", "current_industry": "AI/ML"}, "career_history": [{"company": "Krutrim", "title": "Machine Learning Engineer", "start_date": "2022-11-14", "end_date": null, "duration_months": 43, "is_current": true, "industry": "AI/ML", "company_size": "201-500", "description": "Owned the ranking layer for an e-commerce search product, evolving it from a hand-tuned scoring function to a learning-to-rank model over 9 months. Designed the relevance labeling pipeline (mix of click-through data and explicit human judgments), the feature pipeline, and the training/eval workflow. Most of the work was infrastructure and data quality \u2014 the modeling part was almost the easy bit. Final model improved revenue-per-search by 12%."}, {"company": "Google", "title": "Recommendation Systems Engineer", "start_date": "2021-12-19", "end_date": "2022-11-14", "duration_months": 11, "is_current": false, "industry": "Internet", "company_size": "10001+", "description": "Built a content recommendation system serving 10M+ users that combined collaborative filtering with content-based ranking. The system uses item-item similarity (via sentence-transformer embeddings) for cold starts and a gradient-boosted model trained on engagement signals for warm users. Most of my time went into the feature pipeline (~200 features) and the A/B testing infrastructure. The launch improved 7-day retention by 6% and time spent per session by 14%."}], "education": [{"institution": "NIT Warangal", "degree": "M.S.", "field_of_study": "Computer Engineering", "start_year": 2013, "end_year": 2016, "grade": "7.21 CGPA", "tier": "tier_1"}], "skills": [{"name": "Haystack", "proficiency": "advanced", "endorsements": 54, "duration_months": 25}, {"name": "PEFT", "proficiency": "expert", "endorsements": 15, "duration_months": 84}, {"name": "Pinecone", "proficiency": "expert", "endorsements": 46, "duration_months": 65}, {"name": "Time Series", "proficiency": "advanced", "endorsements": 35, "duration_months": 33}, {"name": "Machine Learning", "proficiency": "expert", "endorsements": 41, "duration_months": 78}, {"name": "PyTorch", "proficiency": "advanced", "endorsements": 49, "duration_months": 28}, {"name": "Flask", "proficiency": "intermediate", "endorsements": 7, "duration_months": 30}, {"name": "Qdrant", "proficiency": "advanced", "endorsements": 14, "duration_months": 46}, {"name": "Kubeflow", "proficiency": "intermediate", "endorsements": 14, "duration_months": 17}, {"name": "LangChain", "proficiency": "advanced", "endorsements": 50, "duration_months": 40}, {"name": "Deep Learning", "proficiency": "advanced", "endorsements": 40, "duration_months": 34}, {"name": "NLP", "proficiency": "expert", "endorsements": 34, "duration_months": 58}, {"name": "GANs", "proficiency": "intermediate", "endorsements": 0, "duration_months": 33}, {"name": "BM25", "proficiency": "advanced", "endorsements": 0, "duration_months": 47}, {"name": "OpenSearch", "proficiency": "expert", "endorsements": 34, "duration_months": 36}, {"name": "Elasticsearch", "proficiency": "expert", "endorsements": 52, "duration_months": 42}, {"name": "REST APIs", "proficiency": "intermediate", "endorsements": 5, "duration_months": 15}], "certifications": [{"name": "NLP Specialization", "issuer": "Coursera/DeepLearning.AI", "year": 2025}], "languages": [{"language": "English", "proficiency": "native"}, {"language": "Hindi", "proficiency": "native"}], "redrob_signals": {"profile_completeness_score": 97.7, "signup_date": "2025-10-22", "last_active_date": "2026-05-21", "open_to_work_flag": false, "profile_views_received_30d": 181, "applications_submitted_30d": 5, "recruiter_response_rate": 0.88, "avg_response_time_hours": 47.8, "skill_assessment_scores": {}, "connection_count": 1289, "endorsements_received": 57, "notice_period_days": 15, "expected_salary_range_inr_lpa": {"min": 27.4, "max": 46.8}, "preferred_work_mode": "flexible", "willing_to_relocate": false, "github_activity_score": 56.5, "search_appearance_30d": 851, "saved_by_recruiters_30d": 19, "interview_completion_rate": 0.85, "offer_acceptance_rate": 0.51, "verified_email": true, "verified_phone": true, "linkedin_connected": true}}
{"candidate_id": "CAND_0016163", "profile": {"anonymized_name": "Suresh Shah", "headline": "Applied ML Engineer | Applied ML | Building intelligent products", "summary": "Machine learning engineer with 6.7 years of experience building ML-powered features in production. Strong background in NLP, recommendation systems, and applied AI; comfortable across the ML stack from feature engineering through deployment. Recently, I led the team that migrated our keyword-search-based product to embedding-based retrieval. I've spent enough time debugging production ranking issues to know which signals matter and which are noise. My academic background is in CS/ML but my main learning has come from shipping real systems and seeing what holds up under production load. Open to senior IC roles in applied ML or AI engineering, ideally at product companies where I'd own a meaningful piece of the ML stack.", "location": "Gurgaon, Haryana", "country": "India", "years_of_experience": 6.7, "current_title": "Applied ML Engineer", "current_company": "Dream11", "current_company_size": "1001-5000", "current_industry": "Gaming"}, "career_history": [{"company": "Dream11", "title": "Applied ML Engineer", "start_date": "2024-03-08", "end_date": null, "duration_months": 27, "is_current": true, "industry": "Gaming", "company_size": "1001-5000", "description": "Built and operated production ML pipelines using MLflow for experiment tracking, Kubeflow for orchestration, and our internal feature store. My main project was a churn prediction model that's now used by the customer success team to prioritize outreach. Designed the model monitoring stack: data drift detection, prediction distribution checks, and alerting. Mentored a junior engineer through their first end-to-end ML project last year."}, {"company": "Verloop.io", "title": "Applied ML Engineer", "start_date": "2020-11-24", "end_date": "2024-03-08", "duration_months": 40, "is_current": false, "industry": "Conversational AI", "company_size": "51-200", "description": "Developed a semantic search feature for an internal knowledge base of ~500K documents. Used sentence-transformers (all-MiniLM-L6-v2 initially, later upgraded to bge-base) with FAISS for fast nearest-neighbor retrieval. Designed the query expansion module that handles vocabulary mismatch between user queries and document terms. Reported search-relevance improvement of 35% over the prior Elasticsearch BM25 setup, validated through human relevance judgments."}, {"company": "Amazon", "title": "NLP Engineer", "start_date": "2019-11-16", "end_date": "2020-11-10", "duration_months": 12, "is_current": false, "industry": "Internet", "company_size": "10001+", "description": "Built and operated production ML pipelines using MLflow for experiment tracking, Kubeflow for orchestration, and our internal feature store. My main project was a churn prediction model that's now used by the customer success team to prioritize outreach. Designed the model monitoring stack: data drift detection, prediction distribution checks, and alerting. Mentored a junior engineer through their first end-to-end ML project last year."}], "education": [{"institution": "Jadavpur University", "degree": "M.E.", "field_of_study": "Machine Learning", "start_year": 2009, "end_year": 2013, "grade": "9.37 CGPA", "tier": "tier_2"}], "skills": [{"name": "Hugging Face Transformers", "proficiency": "advanced", "endorsements": 14, "duration_months": 41}, {"name": "Computer Vision", "proficiency": "advanced", "endorsements": 32, "duration_months": 47}, {"name": "gRPC", "proficiency": "intermediate", "endorsements": 0, "duration_months": 33}, {"name": "Time Series", "proficiency": "advanced", "endorsements": 53, "duration_months": 45}, {"name": "Weaviate", "proficiency": "expert", "endorsements": 6, "duration_months": 36}, {"name": "CNN", "proficiency": "intermediate", "endorsements": 15, "duration_months": 33}, {"name": "Deep Learning", "proficiency": "advanced", "endorsements": 1, "duration_months": 30}, {"name": "TensorFlow", "proficiency": "expert", "endorsements": 45, "duration_months": 83}, {"name": "ASR", "proficiency": "intermediate", "endorsements": 4, "duration_months": 36}, {"name": "Embeddings", "proficiency": "advanced", "endorsements": 17, "duration_months": 48}, {"name": "Tailwind", "proficiency": "intermediate", "endorsements": 15, "duration_months": 9}, {"name": "Reinforcement Learning", "proficiency": "advanced", "endorsements": 25, "duration_months": 24}, {"name": "LoRA", "proficiency": "advanced", "endorsements": 56, "duration_months": 40}, {"name": "pgvector", "proficiency": "expert", "endorsements": 53, "duration_months": 64}, {"name": "Statistical Modeling", "proficiency": "intermediate", "endorsements": 11, "duration_months": 21}, {"name": "Pinecone", "proficiency": "advanced", "endorsements": 29, "duration_months": 40}, {"name": "Machine Learning", "proficiency": "expert", "endorsements": 37, "duration_months": 69}], "certifications": [], "languages": [{"language": "English", "proficiency": "professional"}, {"language": "Hindi", "proficiency": "native"}], "redrob_signals": {"profile_completeness_score": 89.5, "signup_date": "2024-05-02", "last_active_date": "2026-04-11", "open_to_work_flag": true, "profile_views_received_30d": 96, "applications_submitted_30d": 10, "recruiter_response_rate": 0.72, "avg_response_time_hours": 3.6, "skill_assessment_scores": {"Hugging Face Transformers": 53.3, "Computer Vision": 86.4, "Time Series": 89.1, "Weaviate": 88.3}, "connection_count": 1110, "endorsements_received": 11, "notice_period_days": 120, "expected_salary_range_inr_lpa": {"min": 33.0, "max": 44.8}, "preferred_work_mode": "remote", "willing_to_relocate": false, "github_activity_score": 95.0, "search_appearance_30d": 1020, "saved_by_recruiters_30d": 13, "interview_completion_rate": 0.62, "offer_acceptance_rate": 0.85, "verified_email": true, "verified_phone": true, "linkedin_connected": true}}
{"candidate_id": "CAND_0083307", "profile": {"anonymized_name": "Neha Patel", "headline": "Search Engineer | Search, Ranking & Retrieval", "summary": "Machine learning engineer with 7.8 years of experience building ML-powered features in production. Strong background in NLP, recommendation systems, and applied AI; comfortable across the ML stack from feature engineering through deployment. Recently, I shipped our first RAG-based feature this year and now own the eval framework for it. Along the way I've gotten comfortable with the operational side \u2014 A/B testing, drift monitoring, retraining schedules. My academic background is in CS/ML but my main learning has come from shipping real systems and seeing what holds up under production load. Open to senior IC roles in applied ML or AI engineering, ideally at product companies where I'd own a meaningful piece of the ML stack.", "location": "Vizag, Andhra Pradesh", "country": "India", "years_of_experience": 7.8, "current_title": "Search Engineer", "current_company": "CRED", "current_company_size": "1001-5000", "current_industry": "Fintech"}, "career_history": [{"company": "CRED", "title": "Search Engineer", "start_date": "2024-10-04", "end_date": null, "duration_months": 20, "is_current": true, "industry": "Fintech", "company_size": "1001-5000", "description": "Implemented a RAG-based customer support chatbot integrated with our existing ticketing system. Built the document ingestion pipeline (chunking, embedding via OpenAI embeddings, storing in Pinecone) and the answer-generation layer (initially GPT-4, then a fine-tuned smaller model for cost control). Designed the evaluation framework with both automatic metrics (BLEU, ROUGE) and human-in-the-loop quality scores. Deployment cut average ticket resolution time by 31% for the supported categories."}, {"company": "Netflix", "title": "Machine Learning Engineer", "start_date": "2020-07-27", "end_date": "2024-10-04", "duration_months": 51, "is_current": false, "industry": "Media", "company_size": "10001+", "description": "Owned the ranking layer for an e-commerce search product, evolving it from a hand-tuned scoring function to a learning-to-rank model over 9 months. Designed the relevance labeling pipeline (mix of click-through data and explicit human judgments), the feature pipeline, and the training/eval workflow. Most of the work was infrastructure and data quality \u2014 the modeling part was almost the easy bit. Final model improved revenue-per-search by 12%."}, {"company": "Ola", "title": "Machine Learning Engineer", "start_date": "2019-05-27", "end_date": "2020-07-20", "duration_months": 14, "is_current": false, "industry": "Transportation", "company_size": "5001-10000", "description": "Developed a semantic search feature for an internal knowledge base of ~500K documents. Used sentence-transformers (all-MiniLM-L6-v2 initially, later upgraded to bge-base) with FAISS for fast nearest-neighbor retrieval. Designed the query expansion module that handles vocabulary mismatch between user queries and document terms. Reported search-relevance improvement of 35% over the prior Elasticsearch BM25 setup, validated through human relevance judgments."}, {"company": "Saarthi.ai", "title": "NLP Engineer", "start_date": "2018-10-15", "end_date": "2019-05-13", "duration_months": 7, "is_current": false, "industry": "Voice AI", "company_size": "11-50", "description": "Owned the ranking layer for an e-commerce search product, evolving it from a hand-tuned scoring function to a learning-to-rank model over 9 months. Designed the relevance labeling pipeline (mix of click-through data and explicit human judgments), the feature pipeline, and the training/eval workflow. Most of the work was infrastructure and data quality \u2014 the modeling part was almost the easy bit. Final model improved revenue-per-search by 12%."}], "education": [{"institution": "COEP Pune", "degree": "B.Sc", "field_of_study": "Machine Learning", "start_year": 2007, "end_year": 2011, "grade": "8.89 CGPA", "tier": "tier_2"}, {"institution": "VIT Chennai", "degree": "Ph.D", "field_of_study": "Computer Engineering", "start_year": 2012, "end_year": 2015, "grade": "90%", "tier": "tier_3"}], "skills": [{"name": "OpenCV", "proficiency": "advanced", "endorsements": 24, "duration_months": 57}, {"name": "scikit-learn", "proficiency": "advanced", "endorsements": 53, "duration_months": 19}, {"name": "Embeddings", "proficiency": "expert", "endorsements": 5, "duration_months": 83}, {"name": "Image Classification", "proficiency": "intermediate", "endorsements": 8, "duration_months": 32}, {"name": "Weights & Biases", "proficiency": "intermediate", "endorsements": 5, "duration_months": 36}, {"name": "QLoRA", "proficiency": "expert", "endorsements": 11, "duration_months": 36}, {"name": "PEFT", "proficiency": "expert", "endorsements": 35, "duration_months": 92}, {"name": "Pinecone", "proficiency": "expert", "endorsements": 14, "duration_months": 76}, {"name": "GCP", "proficiency": "beginner", "endorsements": 12, "duration_months": 13}, {"name": "Weaviate", "proficiency": "expert", "endorsements": 58, "duration_months": 64}, {"name": "Semantic Search", "proficiency": "advanced", "endorsements": 20, "duration_months": 57}, {"name": "Python", "proficiency": "expert", "endorsements": 55, "duration_months": 88}, {"name": "Learning to Rank", "proficiency": "advanced", "endorsements": 19, "duration_months": 37}, {"name": "MLflow", "proficiency": "advanced", "endorsements": 16, "duration_months": 19}, {"name": "Vector Search", "proficiency": "expert", "endorsements": 33, "duration_months": 81}, {"name": "pgvector", "proficiency": "expert", "endorsements": 7, "duration_months": 60}, {"name": "Deep Learning", "proficiency": "expert", "endorsements": 2, "duration_months": 74}, {"name": "Data Science", "proficiency": "intermediate", "endorsements": 9, "duration_months": 16}], "certifications": [], "languages": [{"language": "English", "proficiency": "native"}, {"language": "Hindi", "proficiency": "conversational"}], "redrob_signals": {"profile_completeness_score": 53.0, "signup_date": "2024-09-16", "last_active_date": "2026-03-21", "open_to_work_flag": true, "profile_views_received_30d": 95, "applications_submitted_30d": 22, "recruiter_response_rate": 0.7, "avg_response_time_hours": 25.7, "skill_assessment_scores": {}, "connection_count": 427, "endorsements_received": 145, "notice_period_days": 120, "expected_salary_range_inr_lpa": {"min": 23.8, "max": 38.7}, "preferred_work_mode": "flexible", "willing_to_relocate": false, "github_activity_score": 69.2, "search_appearance_30d": 233, "saved_by_recruiters_30d": 28, "interview_completion_rate": 0.83, "offer_acceptance_rate": 0.51, "verified_email": true, "verified_phone": true, "linkedin_connected": true}}
{"candidate_id": "CAND_0000021", "profile": {"anonymized_name": "Rahul Joshi", "headline": "Project Manager | AI enthusiast | Building with LLMs", "summary": "Project Manager with 14.5+ years of experience driving outcomes in my domain. I have built strong functional expertise in the typical responsibilities of the role, including team management, stakeholder communication, and project delivery. Recently I've been excited about how AI and GenAI tools can augment my work. I've been taking online courses on RAG and vector databases, experimenting with LangChain and the OpenAI API for side projects, and exploring how LLMs can streamline workflows in my current function. Open to roles that combine my existing domain experience with emerging AI technologies \u2014 I think the most interesting opportunities are at this intersection. Looking for positions where I can contribute both my functional expertise and grow my AI capabilities.", "location": "Bhubaneswar, Odisha", "country": "India", "years_of_experience": 14.5, "current_title": "Project Manager", "current_company": "Wipro", "current_company_size": "10001+", "current_industry": "IT Services"}, "career_history": [{"company": "Wipro", "title": "Project Manager", "start_date": "2023-12-09", "end_date": null, "duration_months": 30, "is_current": true, "industry": "IT Services", "company_size": "10001+", "description": "Brand design and creative direction at a consumer-products company. Owned brand identity (logo, visual system, typography), packaging design, and digital creative across web and social. Led the recent rebrand and managed a small external agency for production work. Comfortable across the Adobe suite, Figma, and the production side of brand and packaging design."}, {"company": "Infosys", "title": "Marketing Manager", "start_date": "2021-02-22", "end_date": "2023-10-10", "duration_months": 32, "is_current": false, "industry": "IT Services", "company_size": "10001+", "description": "Mechanical engineering design role at a hardware-product company. Led the design of two product subsystems through full lifecycle: concept, DFM/DFMA review, prototype, production tooling. Comfortable with CAD (SolidWorks, Creo), FEA (ANSYS), and the typical hardware-development cadence. Worked closely with manufacturing partners on production scale-up."}, {"company": "Stark Industries", "title": "Sales Executive", "start_date": "2019-08-25", "end_date": "2021-02-15", "duration_months": 18, "is_current": false, "industry": "Manufacturing", "company_size": "1001-5000", "description": "Customer support team lead at a SaaS product. Managed a team of 8 support agents handling tier-1 and tier-2 tickets; owned the escalation process to engineering and the customer-feedback loop to product. Built out the support knowledge base and the agent training program. Strong on the people-management side and the process side; lighter on technical depth beyond product expertise."}, {"company": "Dunder Mifflin", "title": "Customer Support", "start_date": "2015-06-17", "end_date": "2019-08-25", "duration_months": 51, "is_current": false, "industry": "Paper Products", "company_size": "201-500", "description": "Customer support team lead at a SaaS product. Managed a team of 8 support agents handling tier-1 and tier-2 tickets; owned the escalation process to engineering and the customer-feedback loop to product. Built out the support knowledge base and the agent training program. Strong on the people-management side and the process side; lighter on technical depth beyond product expertise."}, {"company": "Wipro", "title": "Project Manager", "start_date": "2014-03-24", "end_date": "2015-06-17", "duration_months": 15, "is_current": false, "industry": "IT Services", "company_size": "10001+", "description": "Customer support team lead at a SaaS product. Managed a team of 8 support agents handling tier-1 and tier-2 tickets; owned the escalation process to engineering and the customer-feedback loop to product. Built out the support knowledge base and the agent training program. Strong on the people-management side and the process side; lighter on technical depth beyond product expertise."}, {"company": "TCS", "title": "Customer Support", "start_date": "2011-12-05", "end_date": "2014-01-23", "duration_months": 26, "is_current": false, "industry": "IT Services", "company_size": "10001+", "description": "Business analyst at a consulting firm, working primarily with retail and CPG clients. Conducted business diagnostics, process re-engineering work, and digital transformation strategy projects. Strong on stakeholder management, structured problem-solving, and the typical consulting toolkit (slide-craft, Excel modeling, executive communication). Recent project work involved AI-strategy advisory but my own technical depth in AI is limited."}], "education": [{"institution": "Tier-3 Engineering College", "degree": "B.Tech", "field_of_study": "Artificial Intelligence", "start_year": 2008, "end_year": 2011, "grade": "9.30 CGPA", "tier": "tier_4"}], "skills": [{"name": "Hadoop", "proficiency": "beginner", "endorsements": 10, "duration_months": 5}, {"name": "PostgreSQL", "proficiency": "beginner", "endorsements": 10, "duration_months": 4}, {"name": "Kafka", "proficiency": "beginner", "endorsements": 6, "duration_months": 6}, {"name": "Microservices", "proficiency": "intermediate", "endorsements": 0, "duration_months": 14}, {"name": "AWS", "proficiency": "intermediate", "endorsements": 11, "duration_months": 26}, {"name": "TypeScript", "proficiency": "beginner", "endorsements": 6, "duration_months": 6}, {"name": "ETL", "proficiency": "beginner", "endorsements": 11, "duration_months": 3}, {"name": "Spring Boot", "proficiency": "beginner", "endorsements": 1, "duration_months": 12}, {"name": "Recommendation Systems", "proficiency": "advanced", "endorsements": 3, "duration_months": 13}, {"name": "Fine-tuning LLMs", "proficiency": "advanced", "endorsements": 4, "duration_months": 4}, {"name": "Prompt Engineering", "proficiency": "advanced", "endorsements": 4, "duration_months": 5}, {"name": "LangChain", "proficiency": "intermediate", "endorsements": 1, "duration_months": 7}, {"name": "Pinecone", "proficiency": "intermediate", "endorsements": 4, "duration_months": 16}, {"name": "Vector Search", "proficiency": "intermediate", "endorsements": 3, "duration_months": 13}, {"name": "Embeddings", "proficiency": "advanced", "endorsements": 4, "duration_months": 18}, {"name": "FAISS", "proficiency": "intermediate", "endorsements": 2, "duration_months": 8}], "certifications": [], "languages": [{"language": "English", "proficiency": "professional"}, {"language": "Hindi", "proficiency": "professional"}], "redrob_signals": {"profile_completeness_score": 58.5, "signup_date": "2026-02-10", "last_active_date": "2025-11-21", "open_to_work_flag": false, "profile_views_received_30d": 1, "applications_submitted_30d": 8, "recruiter_response_rate": 0.49, "avg_response_time_hours": 98.7, "skill_assessment_scores": {}, "connection_count": 52, "endorsements_received": 3, "notice_period_days": 60, "expected_salary_range_inr_lpa": {"min": 10.9, "max": 24.4}, "preferred_work_mode": "hybrid", "willing_to_relocate": true, "github_activity_score": 6.4, "search_appearance_30d": 8, "saved_by_recruiters_30d": 3, "interview_completion_rate": 0.53, "offer_acceptance_rate": -1, "verified_email": true, "verified_phone": true, "linkedin_connected": true}}
{"candidate_id": "CAND_0000031", "profile": {"anonymized_name": "Ela Singh", "headline": "Recommendation Systems Engineer | Search, Ranking & Retrieval", "summary": "Machine learning engineer with 6.0 years of experience building ML-powered features in production. Strong background in NLP, recommendation systems, and applied AI; comfortable across the ML stack from feature engineering through deployment. Recently, I led the team that migrated our keyword-search-based product to embedding-based retrieval. I've learned that most retrieval problems are actually evaluation problems in disguise. My academic background is in CS/ML but my main learning has come from shipping real systems and seeing what holds up under production load. Open to senior IC roles in applied ML or AI engineering, ideally at product companies where I'd own a meaningful piece of the ML stack.", "location": "Hyderabad, Telangana", "country": "India", "years_of_experience": 6.0, "current_title": "Recommendation Systems Engineer", "current_company": "Swiggy", "current_company_size": "5001-10000", "current_industry": "Food Delivery"}, "career_history": [{"company": "Swiggy", "title": "Recommendation Systems Engineer", "start_date": "2025-04-02", "end_date": null, "duration_months": 14, "is_current": true, "industry": "Food Delivery", "company_size": "5001-10000", "description": "Trained and shipped multiple ranking models for our product's discovery feed using XGBoost and LightGBM. Designed features across three families: content metadata, user behavior signals, and item engagement history. Owned the offline-online correlation analysis that determined which offline metrics actually predicted A/B test outcomes. Worked closely with PMs to define the optimization target (click-through vs. dwell time vs. downstream conversion) \u2014 that work was as important as the modeling itself."}, {"company": "Mad Street Den", "title": "Search Engineer", "start_date": "2023-10-10", "end_date": "2025-02-01", "duration_months": 16, "is_current": false, "industry": "AI/ML", "company_size": "201-500", "description": "Trained and shipped multiple ranking models for our product's discovery feed using XGBoost and LightGBM. Designed features across three families: content metadata, user behavior signals, and item engagement history. Owned the offline-online correlation analysis that determined which offline metrics actually predicted A/B test outcomes. Worked closely with PMs to define the optimization target (click-through vs. dwell time vs. downstream conversion) \u2014 that work was as important as the modeling itself."}, {"company": "Uber", "title": "NLP Engineer", "start_date": "2021-07-22", "end_date": "2023-10-10", "duration_months": 27, "is_current": false, "industry": "Transportation", "company_size": "10001+", "description": "Trained and shipped multiple ranking models for our product's discovery feed using XGBoost and LightGBM. Designed features across three families: content metadata, user behavior signals, and item engagement history. Owned the offline-online correlation analysis that determined which offline metrics actually predicted A/B test outcomes. Worked closely with PMs to define the optimization target (click-through vs. dwell time vs. downstream conversion) \u2014 that work was as important as the modeling itself."}, {"company": "Zomato", "title": "Applied ML Engineer", "start_date": "2020-06-27", "end_date": "2021-07-22", "duration_months": 13, "is_current": false, "industry": "Food Delivery", "company_size": "5001-10000", "description": "Owned the ranking layer for an e-commerce search product, evolving it from a hand-tuned scoring function to a learning-to-rank model over 9 months. Designed the relevance labeling pipeline (mix of click-through data and explicit human judgments), the feature pipeline, and the training/eval workflow. Most of the work was infrastructure and data quality \u2014 the modeling part was almost the easy bit. Final model improved revenue-per-search by 12%."}], "education": [{"institution": "SRM University", "degree": "M.Tech", "field_of_study": "Computer Engineering", "start_year": 2002, "end_year": 2006, "grade": "9.16 CGPA", "tier": "tier_2"}], "skills": [{"name": "Go", "proficiency": "intermediate", "endorsements": 7, "duration_months": 19}, {"name": "MLflow", "proficiency": "advanced", "endorsements": 59, "duration_months": 21}, {"name": "FAISS", "proficiency": "advanced", "endorsements": 19, "duration_months": 35}, {"name": "Pinecone", "proficiency": "expert", "endorsements": 34, "duration_months": 88}, {"name": "Angular", "proficiency": "beginner", "endorsements": 4, "duration_months": 18}, {"name": "Image Classification", "proficiency": "advanced", "endorsements": 56, "duration_months": 28}, {"name": "Machine Learning", "proficiency": "advanced", "endorsements": 43, "duration_months": 23}, {"name": "Speech Recognition", "proficiency": "intermediate", "endorsements": 14, "duration_months": 24}, {"name": "BentoML", "proficiency": "intermediate", "endorsements": 6, "duration_months": 14}, {"name": "MLOps", "proficiency": "intermediate", "endorsements": 15, "duration_months": 36}, {"name": "Embeddings", "proficiency": "expert", "endorsements": 48, "duration_months": 60}, {"name": "Information Retrieval", "proficiency": "expert", "endorsements": 2, "duration_months": 84}, {"name": "Hugging Face Transformers", "proficiency": "expert", "endorsements": 18, "duration_months": 36}, {"name": "Feature Engineering", "proficiency": "advanced", "endorsements": 38, "duration_months": 26}, {"name": "Sentence Transformers", "proficiency": "expert", "endorsements": 16, "duration_months": 69}, {"name": "scikit-learn", "proficiency": "advanced", "endorsements": 41, "duration_months": 60}, {"name": "Marketing", "proficiency": "intermediate", "endorsements": 11, "duration_months": 36}], "certifications": [], "languages": [{"language": "English", "proficiency": "native"}, {"language": "Hindi", "proficiency": "native"}], "redrob_signals": {"profile_completeness_score": 83.4, "signup_date": "2026-01-28", "last_active_date": "2026-05-24", "open_to_work_flag": true, "profile_views_received_30d": 194, "applications_submitted_30d": 2, "recruiter_response_rate": 0.91, "avg_response_time_hours": 76.1, "skill_assessment_scores": {"MLflow": 75.1, "FAISS": 68.4, "Pinecone": 53.6, "Image Classification": 57.1}, "connection_count": 832, "endorsements_received": 177, "notice_period_days": 60, "expected_salary_range_inr_lpa": {"min": 27.3, "max": 60.2}, "preferred_work_mode": "flexible", "willing_to_relocate": true, "github_activity_score": 32.6, "search_appearance_30d": 778, "saved_by_recruiters_30d": 13, "interview_completion_rate": 0.6, "offer_acceptance_rate": 0.38, "verified_email": false, "verified_phone": true, "linkedin_connected": false}}
{"candidate_id": "CAND_0000074", "profile": {"anonymized_name": "Pari Banerjee", "headline": "Operations Manager | Generative AI explorer", "summary": "Operations Manager with 1.9+ years of experience driving outcomes in my domain. I have built strong functional expertise in the typical responsibilities of the role, including team management, stakeholder communication, and project delivery. Recently I've been excited about how AI and GenAI tools can augment my work. I've been taking online courses on RAG and vector databases, experimenting with LangChain and the OpenAI API for side projects, and exploring how LLMs can streamline workflows in my current function. Open to roles that combine my existing domain experience with emerging AI technologies \u2014 I think the most interesting opportunities are at this intersection. Looking for positions where I can contribute both my functional expertise and grow my AI capabilities.", "location": "Indore, Madhya Pradesh", "country": "India", "years_of_experience": 1.9, "current_title": "Operations Manager", "current_company": "Dunder Mifflin", "current_company_size": "201-500", "current_industry": "Paper Products"}, "career_history": [{"company": "Dunder Mifflin", "title": "Operations Manager", "start_date": "2024-08-05", "end_date": null, "duration_months": 22, "is_current": true, "industry": "Paper Products", "company_size": "201-500", "description": "Marketing leadership role at a B2B SaaS company. Owned the demand-generation function \u2014 content marketing, paid acquisition, SEO, email nurture. Built and managed a team of 5 across content, performance marketing, and marketing operations. Worked closely with sales on lead-quality definitions and the SDR-handoff process. Recent focus has been on account-based marketing for our enterprise segment."}], "education": [{"institution": "Generic State University", "degree": "M.Tech", "field_of_study": "Electronics", "start_year": 2013, "end_year": 2018, "grade": "6.99 CGPA", "tier": "tier_4"}, {"institution": "Generic State University", "degree": "M.Sc", "field_of_study": "Statistics", "start_year": 2011, "end_year": 2014, "grade": "73%", "tier": "tier_4"}], "skills": [{"name": "Go", "proficiency": "intermediate", "endorsements": 14, "duration_months": 34}, {"name": "gRPC", "proficiency": "intermediate", "endorsements": 1, "duration_months": 28}, {"name": "Next.js", "proficiency": "beginner", "endorsements": 13, "duration_months": 11}, {"name": "Webpack", "proficiency": "intermediate", "endorsements": 7, "duration_months": 30}, {"name": "AWS", "proficiency": "beginner", "endorsements": 1, "duration_months": 7}, {"name": "Information Retrieval", "proficiency": "intermediate", "endorsements": 4, "duration_months": 17}, {"name": "Hugging Face Transformers", "proficiency": "advanced", "endorsements": 3, "duration_months": 13}, {"name": "Sentence Transformers", "proficiency": "advanced", "endorsements": 0, "duration_months": 9}, {"name": "Recommendation Systems", "proficiency": "advanced", "endorsements": 1, "duration_months": 6}, {"name": "Embeddings", "proficiency": "advanced", "endorsements": 4, "duration_months": 16}, {"name": "RAG", "proficiency": "advanced", "endorsements": 3, "duration_months": 11}, {"name": "LLMs", "proficiency": "advanced", "endorsements": 0, "duration_months": 5}, {"name": "FAISS", "proficiency": "advanced", "endorsements": 4, "duration_months": 18}, {"name": "Vector Search", "proficiency": "advanced", "endorsements": 3, "duration_months": 15}, {"name": "Pinecone", "proficiency": "intermediate", "endorsements": 4, "duration_months": 5}], "certifications": [], "languages": [{"language": "English", "proficiency": "native"}, {"language": "Hindi", "proficiency": "professional"}], "redrob_signals": {"profile_completeness_score": 66.2, "signup_date": "2023-01-16", "last_active_date": "2025-11-03", "open_to_work_flag": false, "profile_views_received_30d": 15, "applications_submitted_30d": 4, "recruiter_response_rate": 0.73, "avg_response_time_hours": 207.5, "skill_assessment_scores": {}, "connection_count": 179, "endorsements_received": 22, "notice_period_days": 60, "expected_salary_range_inr_lpa": {"min": 13.4, "max": 12.7}, "preferred_work_mode": "onsite", "willing_to_relocate": false, "github_activity_score": -1, "search_appearance_30d": 47, "saved_by_recruiters_30d": 2, "interview_completion_rate": 0.83, "offer_acceptance_rate": 0.73, "verified_email": false, "verified_phone": false, "linkedin_connected": false}}
{"candidate_id": "CAND_0000082", "profile": {"anonymized_name": "Avni Malhotra", "headline": "Data Analyst | 7.4+ yrs in data engineering", "summary": "Software / data professional with 7.4 years of experience building data pipelines, backend systems, and analytics infrastructure. Started my career in backend engineering and gradually moved closer to data \u2014 first dashboards, then ETL, now some basic ML. My toolkit is solid on the data engineering side \u2014 Python, SQL, Spark, Airflow, warehouse design \u2014 and I've completed a couple of self-directed ML projects (Kaggle competitions, side projects fine-tuning small models). Interested in transitioning toward more AI/ML-focused work, ideally at a company where I can leverage my existing data-infra skills while learning modern ML practice.", "location": "Seattle", "country": "USA", "years_of_experience": 7.4, "current_title": "Data Analyst", "current_company": "Wipro", "current_company_size": "10001+", "current_industry": "IT Services"}, "career_history": [{"company": "Wipro", "title": "Data Analyst", "start_date": "2023-12-09", "end_date": null, "duration_months": 30, "is_current": true, "industry": "IT Services", "company_size": "10001+", "description": "Implemented streaming data pipelines on Kafka and Spark Streaming for a real-time user-activity processing platform. Designed the schema-registry integration, the watermark/state management approach, and the deduplication logic for late-arriving events. Worked closely with the data science team to make sure feature pipelines aligned with what their models needed. Most of my career has been data engineering, with some adjacent ML exposure."}, {"company": "Zomato", "title": "Senior Data Engineer", "start_date": "2020-10-25", "end_date": "2023-10-10", "duration_months": 36, "is_current": false, "industry": "Food Delivery", "company_size": "5001-10000", "description": "Implemented streaming data pipelines on Kafka and Spark Streaming for a real-time user-activity processing platform. Designed the schema-registry integration, the watermark/state management approach, and the deduplication logic for late-arriving events. Worked closely with the data science team to make sure feature pipelines aligned with what their models needed. Most of my career has been data engineering, with some adjacent ML exposure."}, {"company": "Mindtree", "title": "Backend Engineer", "start_date": "2018-11-05", "end_date": "2020-08-26", "duration_months": 22, "is_current": false, "industry": "IT Services", "company_size": "10001+", "description": "Mixed data science and analytics-engineering role at a marketing-analytics startup. Spent maybe 30% of my time on lightweight ML (clustering, classification, churn prediction in sklearn/XGBoost) and 70% on data infrastructure and dashboards. Comfortable with the modeling work but I wouldn't call myself an ML specialist. Built our experimentation framework that supports the product team's A/B tests."}], "education": [{"institution": "Generic State University", "degree": "B.Tech", "field_of_study": "Machine Learning", "start_year": 2009, "end_year": 2012, "grade": "7.52 CGPA", "tier": "tier_4"}, {"institution": "SRM Chennai", "degree": "B.Tech", "field_of_study": "Mechanical Engineering", "start_year": 2004, "end_year": 2008, "grade": "8.32 CGPA", "tier": "tier_3"}], "skills": [{"name": "GANs", "proficiency": "advanced", "endorsements": 14, "duration_months": 31}, {"name": "REST APIs", "proficiency": "intermediate", "endorsements": 5, "duration_months": 11}, {"name": "TTS", "proficiency": "advanced", "endorsements": 38, "duration_months": 24}, {"name": "HTML", "proficiency": "intermediate", "endorsements": 7, "duration_months": 14}, {"name": "YOLO", "proficiency": "advanced", "endorsements": 46, "duration_months": 46}, {"name": "SAP", "proficiency": "intermediate", "endorsements": 10, "duration_months": 14}, {"name": "Pinecone", "proficiency": "advanced", "endorsements": 9, "duration_months": 31}, {"name": "Reinforcement Learning", "proficiency": "advanced", "endorsements": 10, "duration_months": 41}, {"name": "Embeddings", "proficiency": "advanced", "endorsements": 40, "duration_months": 28}, {"name": "LLMs", "proficiency": "intermediate", "endorsements": 9, "duration_months": 21}, {"name": "FAISS", "proficiency": "intermediate", "endorsements": 9, "duration_months": 24}], "certifications": [{"name": "AWS Certified Cloud Practitioner", "issuer": "AWS", "year": 2022}, {"name": "Six Sigma Green Belt", "issuer": "ASQ", "year": 2024}], "languages": [{"language": "English", "proficiency": "professional"}, {"language": "Hindi", "proficiency": "native"}], "redrob_signals": {"profile_completeness_score": 66.2, "signup_date": "2024-11-14", "last_active_date": "2026-01-23", "open_to_work_flag": true, "profile_views_received_30d": 99, "applications_submitted_30d": 7, "recruiter_response_rate": 0.82, "avg_response_time_hours": 14.4, "skill_assessment_scores": {}, "connection_count": 674, "endorsements_received": 3, "notice_period_days": 120, "expected_salary_range_inr_lpa": {"min": 14.2, "max": 29.0}, "preferred_work_mode": "remote", "willing_to_relocate": false, "github_activity_score": -1, "search_appearance_30d": 331, "saved_by_recruiters_30d": 7, "interview_completion_rate": 0.9, "offer_acceptance_rate": 0.24, "verified_email": true, "verified_phone": true, "linkedin_connected": false}}
{"candidate_id": "CAND_0000083", "profile": {"anonymized_name": "Pooja Malhotra", "headline": "Graphic Designer | Generative AI explorer", "summary": "Graphic Designer with 6.7+ years of experience driving outcomes in my domain. I have built strong functional expertise in the typical responsibilities of the role, including team management, stakeholder communication, and project delivery. Recently I've been excited about how AI and GenAI tools can augment my work. I've been taking online courses on RAG and vector databases, experimenting with LangChain and the OpenAI API for side projects, and exploring how LLMs can streamline workflows in my current function. Open to roles that combine my existing domain experience with emerging AI technologies \u2014 I think the most interesting opportunities are at this intersection. Looking for positions where I can contribute both my functional expertise and grow my AI capabilities.", "location": "Berlin", "country": "Germany", "years_of_experience": 6.7, "current_title": "Graphic Designer", "current_company": "TCS", "current_company_size": "10001+", "current_industry": "IT Services"}, "career_history": [{"company": "TCS", "title": "Graphic Designer", "start_date": "2023-10-10", "end_date": null, "duration_months": 32, "is_current": true, "industry": "IT Services", "company_size": "10001+", "description": "Enterprise sales of cloud software solutions into the mid-market segment. Carried a $1.8M ARR quota and consistently delivered against it across the last three years. Owned the full sales cycle: prospecting, discovery, technical evaluation (with SE support), commercial negotiation, and close. Strong on consultative selling for technical buyers; comfortable engaging with both engineering and finance stakeholders."}, {"company": "Dunder Mifflin", "title": "Graphic Designer", "start_date": "2021-08-14", "end_date": "2023-10-03", "duration_months": 26, "is_current": false, "industry": "Paper Products", "company_size": "201-500", "description": "Customer support team lead at a SaaS product. Managed a team of 8 support agents handling tier-1 and tier-2 tickets; owned the escalation process to engineering and the customer-feedback loop to product. Built out the support knowledge base and the agent training program. Strong on the people-management side and the process side; lighter on technical depth beyond product expertise."}, {"company": "TCS", "title": "Customer Support", "start_date": "2019-11-23", "end_date": "2021-08-14", "duration_months": 21, "is_current": false, "industry": "IT Services", "company_size": "10001+", "description": "Business analyst at a consulting firm, working primarily with retail and CPG clients. Conducted business diagnostics, process re-engineering work, and digital transformation strategy projects. Strong on stakeholder management, structured problem-solving, and the typical consulting toolkit (slide-craft, Excel modeling, executive communication). Recent project work involved AI-strategy advisory but my own technical depth in AI is limited."}], "education": [{"institution": "VIT Chennai", "degree": "Ph.D", "field_of_study": "Computer Science", "start_year": 2014, "end_year": 2017, "grade": "9.02 CGPA", "tier": "tier_3"}], "skills": [{"name": "BigQuery", "proficiency": "beginner", "endorsements": 8, "duration_months": 4}, {"name": "Flask", "proficiency": "intermediate", "endorsements": 11, "duration_months": 22}, {"name": "Figma", "proficiency": "intermediate", "endorsements": 14, "duration_months": 14}, {"name": "SAP", "proficiency": "beginner", "endorsements": 8, "duration_months": 17}, {"name": "Vue.js", "proficiency": "intermediate", "endorsements": 3, "duration_months": 35}, {"name": "Marketing", "proficiency": "beginner", "endorsements": 9, "duration_months": 3}, {"name": "Semantic Search", "proficiency": "advanced", "endorsements": 3, "duration_months": 12}, {"name": "Pinecone", "proficiency": "advanced", "endorsements": 4, "duration_months": 13}, {"name": "Fine-tuning LLMs", "proficiency": "advanced", "endorsements": 2, "duration_months": 18}, {"name": "RAG", "proficiency": "intermediate", "endorsements": 4, "duration_months": 11}, {"name": "Hugging Face Transformers", "proficiency": "advanced", "endorsements": 1, "duration_months": 17}, {"name": "Recommendation Systems", "proficiency": "intermediate", "endorsements": 0, "duration_months": 12}, {"name": "Information Retrieval", "proficiency": "advanced", "endorsements": 3, "duration_months": 18}, {"name": "FAISS", "proficiency": "advanced", "endorsements": 4, "duration_months": 16}, {"name": "LangChain", "proficiency": "advanced", "endorsements": 2, "duration_months": 18}, {"name": "Vector Search", "proficiency": "intermediate", "endorsements": 0, "duration_months": 12}, {"name": "LLMs", "proficiency": "advanced", "endorsements": 2, "duration_months": 15}], "certifications": [{"name": "Six Sigma Green Belt", "issuer": "ASQ", "year": 2023}], "languages": [{"language": "English", "proficiency": "native"}, {"language": "Hindi", "proficiency": "native"}], "redrob_signals": {"profile_completeness_score": 56.6, "signup_date": "2024-02-02", "last_active_date": "2026-05-06", "open_to_work_flag": false, "profile_views_received_30d": 59, "applications_submitted_30d": 10, "recruiter_response_rate": 0.49, "avg_response_time_hours": 209.6, "skill_assessment_scores": {}, "connection_count": 247, "endorsements_received": 2, "notice_period_days": 90, "expected_salary_range_inr_lpa": {"min": 13.0, "max": 7.0}, "preferred_work_mode": "flexible", "willing_to_relocate": false, "github_activity_score": 39.0, "search_appearance_30d": 163, "saved_by_recruiters_30d": 7, "interview_completion_rate": 0.48, "offer_acceptance_rate": 0.22, "verified_email": false, "verified_phone": true, "linkedin_connected": false}}
{"candidate_id": "CAND_0000097", "profile": {"anonymized_name": "Sai Pillai", "headline": "Mechanical Engineer | AI enthusiast | Building with LLMs", "summary": "Mechanical Engineer with 5.8+ years of experience driving outcomes in my domain. I have built strong functional expertise in the typical responsibilities of the role, including team management, stakeholder communication, and project delivery. Recently I've been excited about how AI and GenAI tools can augment my work. I've been taking online courses on RAG and vector databases, experimenting with LangChain and the OpenAI API for side projects, and exploring how LLMs can streamline workflows in my current function. Open to roles that combine my existing domain experience with emerging AI technologies \u2014 I think the most interesting opportunities are at this intersection. Looking for positions where I can contribute both my functional expertise and grow my AI capabilities.", "location": "Indore, Madhya Pradesh", "country": "India", "years_of_experience": 5.8, "current_title": "Mechanical Engineer", "current_company": "TCS", "current_company_size": "10001+", "current_industry": "IT Services"}, "career_history": [{"company": "TCS", "title": "Mechanical Engineer", "start_date": "2022-04-18", "end_date": null, "duration_months": 50, "is_current": true, "industry": "IT Services", "company_size": "10001+", "description": "Marketing leadership role at a B2B SaaS company. Owned the demand-generation function \u2014 content marketing, paid acquisition, SEO, email nurture. Built and managed a team of 5 across content, performance marketing, and marketing operations. Worked closely with sales on lead-quality definitions and the SDR-handoff process. Recent focus has been on account-based marketing for our enterprise segment."}, {"company": "Initech", "title": "Accountant", "start_date": "2020-09-11", "end_date": "2022-04-04", "duration_months": 19, "is_current": false, "industry": "Software", "company_size": "51-200", "description": "Mechanical engineering design role at a hardware-product company. Led the design of two product subsystems through full lifecycle: concept, DFM/DFMA review, prototype, production tooling. Comfortable with CAD (SolidWorks, Creo), FEA (ANSYS), and the typical hardware-development cadence. Worked closely with manufacturing partners on production scale-up."}], "education": [{"institution": "Regional Technical Institute", "degree": "M.S.", "field_of_study": "MBA", "start_year": 2012, "end_year": 2017, "grade": "6.63 CGPA", "tier": "tier_4"}], "skills": [{"name": "TypeScript", "proficiency": "beginner", "endorsements": 12, "duration_months": 18}, {"name": "SEO", "proficiency": "beginner", "endorsements": 8, "duration_months": 6}, {"name": "Node.js", "proficiency": "intermediate", "endorsements": 3, "duration_months": 23}, {"name": "Tailwind", "proficiency": "beginner", "endorsements": 15, "duration_months": 18}, {"name": "Terraform", "proficiency": "beginner", "endorsements": 15, "duration_months": 9}, {"name": "Apache Beam", "proficiency": "intermediate", "endorsements": 10, "duration_months": 24}, {"name": "MongoDB", "proficiency": "beginner", "endorsements": 8, "duration_months": 6}, {"name": "Information Retrieval", "proficiency": "advanced", "endorsements": 0, "duration_months": 4}, {"name": "Semantic Search", "proficiency": "advanced", "endorsements": 1, "duration_months": 4}, {"name": "Sentence Transformers", "proficiency": "intermediate", "endorsements": 1, "duration_months": 15}, {"name": "Hugging Face Transformers", "proficiency": "intermediate", "endorsements": 2, "duration_months": 16}, {"name": "RAG", "proficiency": "advanced", "endorsements": 2, "duration_months": 16}, {"name": "Pinecone", "proficiency": "advanced", "endorsements": 1, "duration_months": 15}, {"name": "Prompt Engineering", "proficiency": "advanced", "endorsements": 1, "duration_months": 12}, {"name": "Vector Search", "proficiency": "advanced", "endorsements": 4, "duration_months": 11}, {"name": "FAISS", "proficiency": "intermediate", "endorsements": 1, "duration_months": 5}, {"name": "LLMs", "proficiency": "advanced", "endorsements": 3, "duration_months": 12}], "certifications": [{"name": "Scrum Master Certified", "issuer": "Scrum Alliance", "year": 2020}, {"name": "Six Sigma Green Belt", "issuer": "ASQ", "year": 2019}], "languages": [{"language": "English", "proficiency": "native"}, {"language": "Hindi", "proficiency": "professional"}], "redrob_signals": {"profile_completeness_score": 30.3, "signup_date": "2023-04-08", "last_active_date": "2025-11-30", "open_to_work_flag": false, "profile_views_received_30d": 73, "applications_submitted_30d": 8, "recruiter_response_rate": 0.46, "avg_response_time_hours": 35.3, "skill_assessment_scores": {}, "connection_count": 82, "endorsements_received": 40, "notice_period_days": 30, "expected_salary_range_inr_lpa": {"min": 11.0, "max": 22.5}, "preferred_work_mode": "onsite", "willing_to_relocate": false, "github_activity_score": -1, "search_appearance_30d": 71, "saved_by_recruiters_30d": 0, "interview_completion_rate": 0.48, "offer_acceptance_rate": -1, "verified_email": true, "verified_phone": true, "linkedin_connected": true}}
{"candidate_id": "CAND_0000112", "profile": {"anonymized_name": "Kavya Arora", "headline": "AI Specialist | Building ML-powered solutions", "summary": "Data scientist / ML engineer with 3.8 years of experience in applied machine learning. Worked across predictive modeling, NLP, analytics, and lightweight deployment workflows. I've spent the last couple of years building NLP-based classification and information extraction pipelines. I'm strongest at the modeling and analysis side; comfortable with Python, scikit-learn, pandas, and standard MLOps tooling, but I'm still building depth on the engineering and infra side of production ML. Looking for a role where I can step up to more end-to-end ownership of ML systems, not just modeling.", "location": "Vizag, Andhra Pradesh", "country": "India", "years_of_experience": 3.8, "current_title": "AI Specialist", "current_company": "Zomato", "current_company_size": "5001-10000", "current_industry": "Food Delivery"}, "career_history": [{"company": "Zomato", "title": "AI Specialist", "start_date": "2022-09-15", "end_date": null, "duration_months": 45, "is_current": true, "industry": "Food Delivery", "company_size": "5001-10000", "description": "Built NLP pipelines for sentiment analysis and document classification \u2014 primarily for an internal feedback-analytics dashboard. Started with sklearn-based bag-of-words models, then moved to transformer-based classifiers (DistilBERT) for the harder classes. Comfortable with PyTorch and Hugging Face but most of my training experience has been on small datasets and pre-trained model fine-tuning, not from-scratch model design."}], "education": [{"institution": "IIT Madras", "degree": "B.Sc", "field_of_study": "Computer Science", "start_year": 2009, "end_year": 2014, "grade": "8.14 CGPA", "tier": "tier_1"}], "skills": [{"name": "Deep Learning", "proficiency": "intermediate", "endorsements": 11, "duration_months": 27}, {"name": "TensorFlow", "proficiency": "intermediate", "endorsements": 13, "duration_months": 11}, {"name": "Time Series", "proficiency": "intermediate", "endorsements": 4, "duration_months": 16}, {"name": "Speech Recognition", "proficiency": "advanced", "endorsements": 17, "duration_months": 33}, {"name": "GANs", "proficiency": "advanced", "endorsements": 18, "duration_months": 58}, {"name": "ASR", "proficiency": "intermediate", "endorsements": 6, "duration_months": 13}, {"name": "LlamaIndex", "proficiency": "advanced", "endorsements": 47, "duration_months": 32}, {"name": "Object Detection", "proficiency": "intermediate", "endorsements": 2, "duration_months": 32}, {"name": "Haystack", "proficiency": "intermediate", "endorsements": 3, "duration_months": 36}, {"name": "Go", "proficiency": "intermediate", "endorsements": 14, "duration_months": 33}, {"name": "Hugging Face Transformers", "proficiency": "advanced", "endorsements": 33, "duration_months": 49}, {"name": "Redux", "proficiency": "intermediate", "endorsements": 12, "duration_months": 26}, {"name": "Feature Engineering", "proficiency": "intermediate", "endorsements": 5, "duration_months": 11}, {"name": "PostgreSQL", "proficiency": "beginner", "endorsements": 5, "duration_months": 5}, {"name": "Recommendation Systems", "proficiency": "intermediate", "endorsements": 6, "duration_months": 27}, {"name": "Milvus", "proficiency": "advanced", "endorsements": 29, "duration_months": 30}, {"name": "MLflow", "proficiency": "intermediate", "endorsements": 14, "duration_months": 35}, {"name": "YOLO", "proficiency": "intermediate", "endorsements": 8, "duration_months": 16}], "certifications": [], "languages": [{"language": "English", "proficiency": "professional"}, {"language": "Hindi", "proficiency": "conversational"}], "redrob_signals": {"profile_completeness_score": 82.5, "signup_date": "2024-09-19", "last_active_date": "2026-03-26", "open_to_work_flag": false, "profile_views_received_30d": 129, "applications_submitted_30d": 12, "recruiter_response_rate": 0.31, "avg_response_time_hours": 54.2, "skill_assessment_scores": {"Speech Recognition": 62.9, "GANs": 64.2}, "connection_count": 918, "endorsements_received": 97, "notice_period_days": 45, "expected_salary_range_inr_lpa": {"min": 36.6, "max": 34.9}, "preferred_work_mode": "onsite", "willing_to_relocate": true, "github_activity_score": 79.4, "search_appearance_30d": 22, "saved_by_recruiters_30d": 4, "interview_completion_rate": 0.9, "offer_acceptance_rate": 0.27, "verified_email": true, "verified_phone": true, "linkedin_connected": false}}
{"candidate_id": "CAND_0000120", "profile": {"anonymized_name": "Neha Dutta", "headline": "Graphic Designer | Exploring AI & GenAI applications", "summary": "Graphic Designer with 5.7+ years of experience driving outcomes in my domain. I have built strong functional expertise in the typical responsibilities of the role, including team management, stakeholder communication, and project delivery. Recently I've been excited about how AI and GenAI tools can augment my work. I've been taking online courses on RAG and vector databases, experimenting with LangChain and the OpenAI API for side projects, and exploring how LLMs can streamline workflows in my current function. Open to roles that combine my existing domain experience with emerging AI technologies \u2014 I think the most interesting opportunities are at this intersection. Looking for positions where I can contribute both my functional expertise and grow my AI capabilities.", "location": "Bhubaneswar, Odisha", "country": "India", "years_of_experience": 5.7, "current_title": "Graphic Designer", "current_company": "Wayne Enterprises", "current_company_size": "10001+", "current_industry": "Conglomerate"}, "career_history": [{"company": "Wayne Enterprises", "title": "Graphic Designer", "start_date": "2024-03-08", "end_date": null, "duration_months": 27, "is_current": true, "industry": "Conglomerate", "company_size": "10001+", "description": "Mechanical engineering design role at a hardware-product company. Led the design of two product subsystems through full lifecycle: concept, DFM/DFMA review, prototype, production tooling. Comfortable with CAD (SolidWorks, Creo), FEA (ANSYS), and the typical hardware-development cadence. Worked closely with manufacturing partners on production scale-up."}, {"company": "Pied Piper", "title": "Accountant", "start_date": "2021-11-19", "end_date": "2024-03-08", "duration_months": 28, "is_current": false, "industry": "Software", "company_size": "11-50", "description": "Marketing leadership role at a B2B SaaS company. Owned the demand-generation function \u2014 content marketing, paid acquisition, SEO, email nurture. Built and managed a team of 5 across content, performance marketing, and marketing operations. Worked closely with sales on lead-quality definitions and the SDR-handoff process. Recent focus has been on account-based marketing for our enterprise segment."}, {"company": "Stark Industries", "title": "Sales Executive", "start_date": "2020-09-25", "end_date": "2021-09-20", "duration_months": 12, "is_current": false, "industry": "Manufacturing", "company_size": "1001-5000", "description": "Brand design and creative direction at a consumer-products company. Owned brand identity (logo, visual system, typography), packaging design, and digital creative across web and social. Led the recent rebrand and managed a small external agency for production work. Comfortable across the Adobe suite, Figma, and the production side of brand and packaging design."}], "education": [{"institution": "Georgia Tech", "degree": "B.E.", "field_of_study": "Information Technology", "start_year": 2015, "end_year": 2018, "grade": "8.04 CGPA", "tier": "tier_1"}], "skills": [{"name": "FastAPI", "proficiency": "beginner", "endorsements": 13, "duration_months": 6}, {"name": "AWS", "proficiency": "beginner", "endorsements": 12, "duration_months": 7}, {"name": "Redux", "proficiency": "beginner", "endorsements": 7, "duration_months": 9}, {"name": "Marketing", "proficiency": "intermediate", "endorsements": 0, "duration_months": 27}, {"name": "SQL", "proficiency": "beginner", "endorsements": 0, "duration_months": 9}, {"name": "React", "proficiency": "beginner", "endorsements": 1, "duration_months": 6}, {"name": "Salesforce CRM", "proficiency": "intermediate", "endorsements": 9, "duration_months": 35}, {"name": "Forecasting", "proficiency": "intermediate", "endorsements": 3, "duration_months": 24}, {"name": "Content Writing", "proficiency": "intermediate", "endorsements": 10, "duration_months": 14}, {"name": "RAG", "proficiency": "intermediate", "endorsements": 4, "duration_months": 16}, {"name": "Hugging Face Transformers", "proficiency": "advanced", "endorsements": 2, "duration_months": 16}, {"name": "Vector Search", "proficiency": "advanced", "endorsements": 3, "duration_months": 9}, {"name": "LangChain", "proficiency": "intermediate", "endorsements": 2, "duration_months": 14}, {"name": "Semantic Search", "proficiency": "advanced", "endorsements": 4, "duration_months": 6}, {"name": "Information Retrieval", "proficiency": "advanced", "endorsements": 2, "duration_months": 6}, {"name": "LLMs", "proficiency": "intermediate", "endorsements": 2, "duration_months": 7}, {"name": "Fine-tuning LLMs", "proficiency": "intermediate", "endorsements": 2, "duration_months": 4}, {"name": "Sentence Transformers", "proficiency": "advanced", "endorsements": 1, "duration_months": 6}, {"name": "Embeddings", "proficiency": "advanced", "endorsements": 1, "duration_months": 13}, {"name": "Recommendation Systems", "proficiency": "advanced", "endorsements": 3, "duration_months": 13}], "certifications": [], "languages": [{"language": "English", "proficiency": "native"}, {"language": "Hindi", "proficiency": "conversational"}], "redrob_signals": {"profile_completeness_score": 69.0, "signup_date": "2024-11-27", "last_active_date": "2026-03-10", "open_to_work_flag": false, "profile_views_received_30d": 59, "applications_submitted_30d": 2, "recruiter_response_rate": 0.58, "avg_response_time_hours": 250.4, "skill_assessment_scores": {}, "connection_count": 564, "endorsements_received": 38, "notice_period_days": 60, "expected_salary_range_inr_lpa": {"min": 9.0, "max": 15.6}, "preferred_work_mode": "hybrid", "willing_to_relocate": false, "github_activity_score": -1, "search_appearance_30d": 130, "saved_by_recruiters_30d": 8, "interview_completion_rate": 0.41, "offer_acceptance_rate": -1, "verified_email": false, "verified_phone": true, "linkedin_connected": false}}
{"candidate_id": "CAND_0000121", "profile": {"anonymized_name": "Aarohi Bansal", "headline": "Customer Support | Generative AI explorer", "summary": "Customer Support with 3.7+ years of experience driving outcomes in my domain. I have built strong functional expertise in the typical responsibilities of the role, including team management, stakeholder communication, and project delivery. Recently I've been excited about how AI and GenAI tools can augment my work. I've been taking online courses on RAG and vector databases, experimenting with LangChain and the OpenAI API for side projects, and exploring how LLMs can streamline workflows in my current function. Open to roles that combine my existing domain experience with emerging AI technologies \u2014 I think the most interesting opportunities are at this intersection. Looking for positions where I can contribute both my functional expertise and grow my AI capabilities.", "location": "Mumbai, Maharashtra", "country": "India", "years_of_experience": 3.7, "current_title": "Customer Support", "current_company": "Wayne Enterprises", "current_company_size": "10001+", "current_industry": "Conglomerate"}, "career_history": [{"company": "Wayne Enterprises", "title": "Customer Support", "start_date": "2024-03-08", "end_date": null, "duration_months": 27, "is_current": true, "industry": "Conglomerate", "company_size": "10001+", "description": "Mechanical engineering design role at a hardware-product company. Led the design of two product subsystems through full lifecycle: concept, DFM/DFMA review, prototype, production tooling. Comfortable with CAD (SolidWorks, Creo), FEA (ANSYS), and the typical hardware-development cadence. Worked closely with manufacturing partners on production scale-up."}, {"company": "Hooli", "title": "Project Manager", "start_date": "2022-11-14", "end_date": "2024-03-08", "duration_months": 16, "is_current": false, "industry": "Software", "company_size": "1001-5000", "description": "Marketing leadership role at a B2B SaaS company. Owned the demand-generation function \u2014 content marketing, paid acquisition, SEO, email nurture. Built and managed a team of 5 across content, performance marketing, and marketing operations. Worked closely with sales on lead-quality definitions and the SDR-handoff process. Recent focus has been on account-based marketing for our enterprise segment."}], "education": [{"institution": "Delhi College of Engineering", "degree": "M.E.", "field_of_study": "Computer Engineering", "start_year": 2007, "end_year": 2010, "grade": "7.73 CGPA", "tier": "tier_2"}], "skills": [{"name": "Airflow", "proficiency": "beginner", "endorsements": 8, "duration_months": 18}, {"name": "Marketing", "proficiency": "beginner", "endorsements": 13, "duration_months": 8}, {"name": "Excel", "proficiency": "intermediate", "endorsements": 3, "duration_months": 16}, {"name": "SEO", "proficiency": "beginner", "endorsements": 15, "duration_months": 5}, {"name": "Sales", "proficiency": "intermediate", "endorsements": 2, "duration_months": 28}, {"name": "FAISS", "proficiency": "advanced", "endorsements": 0, "duration_months": 11}, {"name": "Vector Search", "proficiency": "advanced", "endorsements": 4, "duration_months": 8}, {"name": "Recommendation Systems", "proficiency": "advanced", "endorsements": 4, "duration_months": 13}, {"name": "RAG", "proficiency": "advanced", "endorsements": 4, "duration_months": 16}, {"name": "Fine-tuning LLMs", "proficiency": "intermediate", "endorsements": 2, "duration_months": 12}, {"name": "Hugging Face Transformers", "proficiency": "advanced", "endorsements": 2, "duration_months": 14}, {"name": "Prompt Engineering", "proficiency": "advanced", "endorsements": 4, "duration_months": 9}, {"name": "Information Retrieval", "proficiency": "intermediate", "endorsements": 0, "duration_months": 17}, {"name": "Pinecone", "proficiency": "intermediate", "endorsements": 2, "duration_months": 16}, {"name": "Sentence Transformers", "proficiency": "intermediate", "endorsements": 3, "duration_months": 17}], "certifications": [{"name": "AWS Certified Cloud Practitioner", "issuer": "AWS", "year": 2024}], "languages": [{"language": "English", "proficiency": "professional"}, {"language": "Hindi", "proficiency": "conversational"}], "redrob_signals": {"profile_completeness_score": 30.6, "signup_date": "2025-10-06", "last_active_date": "2026-03-10", "open_to_work_flag": false, "profile_views_received_30d": 79, "applications_submitted_30d": 4, "recruiter_response_rate": 0.54, "avg_response_time_hours": 169.8, "skill_assessment_scores": {}, "connection_count": 596, "endorsements_received": 39, "notice_period_days": 30, "expected_salary_range_inr_lpa": {"min": 13.0, "max": 15.1}, "preferred_work_mode": "remote", "willing_to_relocate": false, "github_activity_score": -1, "search_appearance_30d": 175, "saved_by_recruiters_30d": 9, "interview_completion_rate": 0.33, "offer_acceptance_rate": -1, "verified_email": true, "verified_phone": true, "linkedin_connected": false}}
{"candidate_id": "CAND_0000133", "profile": {"anonymized_name": "Rahul Sethi", "headline": "Graphic Designer | Generative AI explorer", "summary": "Graphic Designer with 11.7+ years of experience driving outcomes in my domain. I have built strong functional expertise in the typical responsibilities of the role, including team management, stakeholder communication, and project delivery. Recently I've been excited about how AI and GenAI tools can augment my work. I've been taking online courses on RAG and vector databases, experimenting with LangChain and the OpenAI API for side projects, and exploring how LLMs can streamline workflows in my current function. Open to roles that combine my existing domain experience with emerging AI technologies \u2014 I think the most interesting opportunities are at this intersection. Looking for positions where I can contribute both my functional expertise and grow my AI capabilities.", "location": "Jaipur, Rajasthan", "country": "India", "years_of_experience": 11.7, "current_title": "Graphic Designer", "current_company": "Acme Corp", "current_company_size": "201-500", "current_industry": "Manufacturing"}, "career_history": [{"company": "Acme Corp", "title": "Graphic Designer", "start_date": "2023-02-12", "end_date": null, "duration_months": 40, "is_current": true, "industry": "Manufacturing", "company_size": "201-500", "description": "Enterprise sales of cloud software solutions into the mid-market segment. Carried a $1.8M ARR quota and consistently delivered against it across the last three years. Owned the full sales cycle: prospecting, discovery, technical evaluation (with SE support), commercial negotiation, and close. Strong on consultative selling for technical buyers; comfortable engaging with both engineering and finance stakeholders."}, {"company": "Pied Piper", "title": "Business Analyst", "start_date": "2021-12-19", "end_date": "2023-02-12", "duration_months": 14, "is_current": false, "industry": "Software", "company_size": "11-50", "description": "Business analyst at a consulting firm, working primarily with retail and CPG clients. Conducted business diagnostics, process re-engineering work, and digital transformation strategy projects. Strong on stakeholder management, structured problem-solving, and the typical consulting toolkit (slide-craft, Excel modeling, executive communication). Recent project work involved AI-strategy advisory but my own technical depth in AI is limited."}, {"company": "TCS", "title": "Sales Executive", "start_date": "2020-05-28", "end_date": "2021-12-19", "duration_months": 19, "is_current": false, "industry": "IT Services", "company_size": "10001+", "description": "Content writing and SEO strategy for a tech-focused publication. Wrote longform articles on developer tools, cloud platforms, and AI/ML topics \u2014 including some that ranked on the first page of search for high-competition keywords. Managed a freelance writer pool and the editorial calendar. Recent work has been on AI-assisted content production, using LLM tools for research, drafting, and editing while maintaining editorial quality."}, {"company": "Wayne Enterprises", "title": "HR Manager", "start_date": "2017-05-30", "end_date": "2020-05-14", "duration_months": 36, "is_current": false, "industry": "Conglomerate", "company_size": "10001+", "description": "Mechanical engineering design role at a hardware-product company. Led the design of two product subsystems through full lifecycle: concept, DFM/DFMA review, prototype, production tooling. Comfortable with CAD (SolidWorks, Creo), FEA (ANSYS), and the typical hardware-development cadence. Worked closely with manufacturing partners on production scale-up."}, {"company": "Stark Industries", "title": "Graphic Designer", "start_date": "2015-01-11", "end_date": "2017-05-30", "duration_months": 29, "is_current": false, "industry": "Manufacturing", "company_size": "1001-5000", "description": "Senior accounting role at a mid-sized company \u2014 month-end close, financial reporting, statutory compliance (GAAP / Ind-AS), and tax filings. Owned the GL, fixed-asset register, and the audit-readiness function. Managed a team of 3 staff accountants. Built strong process discipline around the close cycle, reducing close time from 12 days to 7 over the last two years."}], "education": [{"institution": "Local Engineering College", "degree": "B.Tech", "field_of_study": "Civil Engineering", "start_year": 2017, "end_year": 2020, "grade": "8.78 CGPA", "tier": "tier_4"}, {"institution": "Generic State University", "degree": "B.Tech", "field_of_study": "Civil Engineering", "start_year": 2009, "end_year": 2014, "grade": "7.59 CGPA", "tier": "tier_4"}], "skills": [{"name": "Airflow", "proficiency": "beginner", "endorsements": 5, "duration_months": 7}, {"name": "Snowflake", "proficiency": "beginner", "endorsements": 0, "duration_months": 13}, {"name": "Node.js", "proficiency": "intermediate", "endorsements": 4, "duration_months": 18}, {"name": "Hadoop", "proficiency": "beginner", "endorsements": 4, "duration_months": 15}, {"name": "Accounting", "proficiency": "beginner", "endorsements": 1, "duration_months": 13}, {"name": "Rust", "proficiency": "beginner", "endorsements": 13, "duration_months": 10}, {"name": "Hugging Face Transformers", "proficiency": "intermediate", "endorsements": 3, "duration_months": 14}, {"name": "LLMs", "proficiency": "advanced", "endorsements": 1, "duration_months": 13}, {"name": "Sentence Transformers", "proficiency": "intermediate", "endorsements": 1, "duration_months": 9}, {"name": "Fine-tuning LLMs", "proficiency": "advanced", "endorsements": 0, "duration_months": 5}, {"name": "Vector Search", "proficiency": "advanced", "endorsements": 1, "duration_months": 6}, {"name": "Semantic Search", "proficiency": "advanced", "endorsements": 0, "duration_months": 18}, {"name": "Pinecone", "proficiency": "advanced", "endorsements": 3, "duration_months": 18}], "certifications": [{"name": "AWS Certified Cloud Practitioner", "issuer": "AWS", "year": 2020}], "languages": [{"language": "English", "proficiency": "professional"}, {"language": "Hindi", "proficiency": "native"}], "redrob_signals": {"profile_completeness_score": 75.1, "signup_date": "2025-02-03", "last_active_date": "2026-01-26", "open_to_work_flag": false, "profile_views_received_30d": 54, "applications_submitted_30d": 6, "recruiter_response_rate": 0.42, "avg_response_time_hours": 172.5, "skill_assessment_scores": {}, "connection_count": 407, "endorsements_received": 17, "notice_period_days": 120, "expected_salary_range_inr_lpa": {"min": 10.8, "max": 22.9}, "preferred_work_mode": "flexible", "willing_to_relocate": false, "github_activity_score": -1, "search_appearance_30d": 111, "saved_by_recruiters_30d": 5, "interview_completion_rate": 0.63, "offer_acceptance_rate": -1, "verified_email": true, "verified_phone": true, "linkedin_connected": false}}
{"candidate_id": "CAND_0000165", "profile": {"anonymized_name": "Suresh Reddy", "headline": "AI Specialist | Building ML-powered solutions", "summary": "Data scientist / ML engineer with 6.8 years of experience in applied machine learning. Worked across predictive modeling, NLP, analytics, and lightweight deployment workflows. I've spent the last couple of years building NLP-based classification and information extraction pipelines. I'm strongest at the modeling and analysis side; comfortable with Python, scikit-learn, pandas, and standard MLOps tooling, but I'm still building depth on the engineering and infra side of production ML. I'm looking to grow into a deeper AI/ML system-building role \u2014 closer to retrieval, LLMs, and modern ranking systems.", "location": "San Francisco", "country": "USA", "years_of_experience": 6.8, "current_title": "AI Specialist", "current_company": "Mad Street Den", "current_company_size": "201-500", "current_industry": "AI/ML"}, "career_history": [{"company": "Mad Street Den", "title": "AI Specialist", "start_date": "2024-08-05", "end_date": null, "duration_months": 22, "is_current": true, "industry": "AI/ML", "company_size": "201-500", "description": "Built computer vision models for our product's image moderation feature using PyTorch \u2014 fine-tuned ResNet variants on a labeled dataset of ~200K images. Set up the training pipeline (data loading, augmentation, evaluation) and the inference service. Most of my project work has been in CV; I'm now interested in transitioning toward NLP/LLM work but my professional experience there is limited."}, {"company": "BYJU'S", "title": "ML Engineer", "start_date": "2019-10-01", "end_date": "2024-07-06", "duration_months": 58, "is_current": false, "industry": "EdTech", "company_size": "10001+", "description": "Contributed to ML feature engineering and model deployment for a fraud-detection product. My main role was engineering: building the Flask-based prediction API, integrating with the feature store, and writing the model-serving observability layer. I worked closely with senior data scientists but my own modeling work was secondary \u2014 I was the production-side engineer."}], "education": [{"institution": "BITS Pilani", "degree": "M.E.", "field_of_study": "Machine Learning", "start_year": 2008, "end_year": 2013, "grade": "79%", "tier": "tier_1"}], "skills": [{"name": "BentoML", "proficiency": "advanced", "endorsements": 0, "duration_months": 23}, {"name": "Information Retrieval", "proficiency": "advanced", "endorsements": 41, "duration_months": 19}, {"name": "Semantic Search", "proficiency": "intermediate", "endorsements": 10, "duration_months": 25}, {"name": "TTS", "proficiency": "intermediate", "endorsements": 9, "duration_months": 32}, {"name": "Next.js", "proficiency": "beginner", "endorsements": 4, "duration_months": 6}, {"name": "Prompt Engineering", "proficiency": "intermediate", "endorsements": 0, "duration_months": 36}, {"name": "Vector Search", "proficiency": "advanced", "endorsements": 34, "duration_months": 32}, {"name": "YOLO", "proficiency": "advanced", "endorsements": 19, "duration_months": 18}, {"name": "Image Classification", "proficiency": "intermediate", "endorsements": 6, "duration_months": 26}, {"name": "Qdrant", "proficiency": "advanced", "endorsements": 1, "duration_months": 18}, {"name": "Sentence Transformers", "proficiency": "intermediate", "endorsements": 14, "duration_months": 32}, {"name": "Airflow", "proficiency": "intermediate", "endorsements": 11, "duration_months": 24}, {"name": "PostgreSQL", "proficiency": "beginner", "endorsements": 11, "duration_months": 4}, {"name": "ASR", "proficiency": "advanced", "endorsements": 44, "duration_months": 25}], "certifications": [{"name": "Deep Learning Specialization", "issuer": "Coursera/DeepLearning.AI", "year": 2021}, {"name": "NLP Specialization", "issuer": "Coursera/DeepLearning.AI", "year": 2019}], "languages": [{"language": "English", "proficiency": "native"}, {"language": "Hindi", "proficiency": "native"}], "redrob_signals": {"profile_completeness_score": 63.8, "signup_date": "2025-05-30", "last_active_date": "2026-05-11", "open_to_work_flag": true, "profile_views_received_30d": 167, "applications_submitted_30d": 16, "recruiter_response_rate": 0.3, "avg_response_time_hours": 90.9, "skill_assessment_scores": {"BentoML": 80.3, "Information Retrieval": 84.7, "Vector Search": 73.8, "YOLO": 72.7}, "connection_count": 556, "endorsements_received": 111, "notice_period_days": 120, "expected_salary_range_inr_lpa": {"min": 25.0, "max": 30.3}, "preferred_work_mode": "hybrid", "willing_to_relocate": true, "github_activity_score": 61.5, "search_appearance_30d": 173, "saved_by_recruiters_30d": 18, "interview_completion_rate": 0.96, "offer_acceptance_rate": 0.78, "verified_email": true, "verified_phone": true, "linkedin_connected": true}}
{"candidate_id": "CAND_0000201", "profile": {"anonymized_name": "Anil Mehta", "headline": "Marketing Manager | AI enthusiast | Building with LLMs", "summary": "Marketing Manager with 14.1+ years of experience driving outcomes in my domain. I have built strong functional expertise in the typical responsibilities of the role, including team management, stakeholder communication, and project delivery. Recently I've been excited about how AI and GenAI tools can augment my work. I've been taking online courses on RAG and vector databases, experimenting with LangChain and the OpenAI API for side projects, and exploring how LLMs can streamline workflows in my current function. Open to roles that combine my existing domain experience with emerging AI technologies \u2014 I think the most interesting opportunities are at this intersection. Looking for positions where I can contribute both my functional expertise and grow my AI capabilities.", "location": "Mumbai, Maharashtra", "country": "India", "years_of_experience": 14.1, "current_title": "Marketing Manager", "current_company": "Stark Industries", "current_company_size": "1001-5000", "current_industry": "Manufacturing"}, "career_history": [{"company": "Stark Industries", "title": "Marketing Manager", "start_date": "2024-04-07", "end_date": null, "duration_months": 26, "is_current": true, "industry": "Manufacturing", "company_size": "1001-5000", "description": "Enterprise sales of cloud software solutions into the mid-market segment. Carried a $1.8M ARR quota and consistently delivered against it across the last three years. Owned the full sales cycle: prospecting, discovery, technical evaluation (with SE support), commercial negotiation, and close. Strong on consultative selling for technical buyers; comfortable engaging with both engineering and finance stakeholders."}, {"company": "Initech", "title": "Business Analyst", "start_date": "2021-09-20", "end_date": "2024-04-07", "duration_months": 31, "is_current": false, "industry": "Software", "company_size": "51-200", "description": "Brand design and creative direction at a consumer-products company. Owned brand identity (logo, visual system, typography), packaging design, and digital creative across web and social. Led the recent rebrand and managed a small external agency for production work. Comfortable across the Adobe suite, Figma, and the production side of brand and packaging design."}, {"company": "Acme Corp", "title": "Marketing Manager", "start_date": "2018-07-08", "end_date": "2021-07-22", "duration_months": 37, "is_current": false, "industry": "Manufacturing", "company_size": "201-500", "description": "Content writing and SEO strategy for a tech-focused publication. Wrote longform articles on developer tools, cloud platforms, and AI/ML topics \u2014 including some that ranked on the first page of search for high-competition keywords. Managed a freelance writer pool and the editorial calendar. Recent work has been on AI-assisted content production, using LLM tools for research, drafting, and editing while maintaining editorial quality."}, {"company": "Pied Piper", "title": "Content Writer", "start_date": "2014-01-30", "end_date": "2018-05-09", "duration_months": 52, "is_current": false, "industry": "Software", "company_size": "11-50", "description": "Customer support team lead at a SaaS product. Managed a team of 8 support agents handling tier-1 and tier-2 tickets; owned the escalation process to engineering and the customer-feedback loop to product. Built out the support knowledge base and the agent training program. Strong on the people-management side and the process side; lighter on technical depth beyond product expertise."}, {"company": "TCS", "title": "Mechanical Engineer", "start_date": "2012-05-10", "end_date": "2014-01-30", "duration_months": 21, "is_current": false, "industry": "IT Services", "company_size": "10001+", "description": "Senior accounting role at a mid-sized company \u2014 month-end close, financial reporting, statutory compliance (GAAP / Ind-AS), and tax filings. Owned the GL, fixed-asset register, and the audit-readiness function. Managed a team of 3 staff accountants. Built strong process discipline around the close cycle, reducing close time from 12 days to 7 over the last two years."}], "education": [{"institution": "Bharati Vidyapeeth", "degree": "M.E.", "field_of_study": "Commerce", "start_year": 2005, "end_year": 2009, "grade": "7.25 CGPA", "tier": "tier_3"}], "skills": [{"name": "Sales", "proficiency": "intermediate", "endorsements": 1, "duration_months": 31}, {"name": "Data Pipelines", "proficiency": "beginner", "endorsements": 8, "duration_months": 17}, {"name": "Vue.js", "proficiency": "intermediate", "endorsements": 13, "duration_months": 9}, {"name": "GraphQL", "proficiency": "beginner", "endorsements": 14, "duration_months": 14}, {"name": "Marketing", "proficiency": "beginner", "endorsements": 1, "duration_months": 16}, {"name": "Apache Flink", "proficiency": "beginner", "endorsements": 12, "duration_months": 8}, {"name": "Information Retrieval", "proficiency": "advanced", "endorsements": 2, "duration_months": 4}, {"name": "Embeddings", "proficiency": "intermediate", "endorsements": 1, "duration_months": 13}, {"name": "Fine-tuning LLMs", "proficiency": "advanced", "endorsements": 0, "duration_months": 9}, {"name": "Hugging Face Transformers", "proficiency": "advanced", "endorsements": 4, "duration_months": 18}, {"name": "LangChain", "proficiency": "intermediate", "endorsements": 2, "duration_months": 7}, {"name": "RAG", "proficiency": "advanced", "endorsements": 3, "duration_months": 5}, {"name": "LLMs", "proficiency": "advanced", "endorsements": 0, "duration_months": 7}], "certifications": [], "languages": [{"language": "English", "proficiency": "professional"}, {"language": "Hindi", "proficiency": "native"}], "redrob_signals": {"profile_completeness_score": 33.8, "signup_date": "2023-10-27", "last_active_date": "2026-05-02", "open_to_work_flag": false, "profile_views_received_30d": 73, "applications_submitted_30d": 2, "recruiter_response_rate": 0.72, "avg_response_time_hours": 17.9, "skill_assessment_scores": {}, "connection_count": 66, "endorsements_received": 29, "notice_period_days": 60, "expected_salary_range_inr_lpa": {"min": 3.3, "max": 20.1}, "preferred_work_mode": "flexible", "willing_to_relocate": true, "github_activity_score": 42.1, "search_appearance_30d": 10, "saved_by_recruiters_30d": 7, "interview_completion_rate": 0.81, "offer_acceptance_rate": -1, "verified_email": false, "verified_phone": false, "linkedin_connected": true}}
{"candidate_id": "CAND_0000203", "profile": {"anonymized_name": "Aarohi Chowdary", "headline": "Operations Manager | AI enthusiast | Building with LLMs", "summary": "Operations Manager with 4.0+ years of experience driving outcomes in my domain. I have built strong functional expertise in the typical responsibilities of the role, including team management, stakeholder communication, and project delivery. Recently I've been excited about how AI and GenAI tools can augment my work. I've been taking online courses on RAG and vector databases, experimenting with LangChain and the OpenAI API for side projects, and exploring how LLMs can streamline workflows in my current function. Open to roles that combine my existing domain experience with emerging AI technologies \u2014 I think the most interesting opportunities are at this intersection. Looking for positions where I can contribute both my functional expertise and grow my AI capabilities.", "location": "Sydney", "country": "Australia", "years_of_experience": 4.0, "current_title": "Operations Manager", "current_company": "Wayne Enterprises", "current_company_size": "10001+", "current_industry": "Conglomerate"}, "career_history": [{"company": "Wayne Enterprises", "title": "Operations Manager", "start_date": "2025-04-02", "end_date": null, "duration_months": 14, "is_current": true, "industry": "Conglomerate", "company_size": "10001+", "description": "Operations management role at a logistics company. Owned daily fulfillment operations across 3 warehouses, managing a team of 80 across receiving, picking, packing, and outbound. Built and tracked the operational KPIs (on-time fulfillment, accuracy, cost per order) and led the continuous improvement initiatives that drove a 22% productivity gain over 18 months."}, {"company": "Pied Piper", "title": "Customer Support", "start_date": "2023-12-25", "end_date": "2025-03-19", "duration_months": 15, "is_current": false, "industry": "Software", "company_size": "11-50", "description": "Brand design and creative direction at a consumer-products company. Owned brand identity (logo, visual system, typography), packaging design, and digital creative across web and social. Led the recent rebrand and managed a small external agency for production work. Comfortable across the Adobe suite, Figma, and the production side of brand and packaging design."}, {"company": "Wipro", "title": "Operations Manager", "start_date": "2022-08-02", "end_date": "2023-12-25", "duration_months": 17, "is_current": false, "industry": "IT Services", "company_size": "10001+", "description": "Senior accounting role at a mid-sized company \u2014 month-end close, financial reporting, statutory compliance (GAAP / Ind-AS), and tax filings. Owned the GL, fixed-asset register, and the audit-readiness function. Managed a team of 3 staff accountants. Built strong process discipline around the close cycle, reducing close time from 12 days to 7 over the last two years."}], "education": [{"institution": "Local Engineering College", "degree": "Ph.D", "field_of_study": "Civil Engineering", "start_year": 2001, "end_year": 2006, "grade": "87%", "tier": "tier_4"}], "skills": [{"name": "Project Management", "proficiency": "intermediate", "endorsements": 13, "duration_months": 22}, {"name": "Terraform", "proficiency": "intermediate", "endorsements": 6, "duration_months": 11}, {"name": "Spark", "proficiency": "intermediate", "endorsements": 14, "duration_months": 36}, {"name": "HTML", "proficiency": "beginner", "endorsements": 13, "duration_months": 18}, {"name": "Node.js", "proficiency": "beginner", "endorsements": 10, "duration_months": 8}, {"name": "GraphQL", "proficiency": "intermediate", "endorsements": 3, "duration_months": 22}, {"name": "CI/CD", "proficiency": "beginner", "endorsements": 4, "duration_months": 2}, {"name": "Kubernetes", "proficiency": "beginner", "endorsements": 13, "duration_months": 16}, {"name": "Sales", "proficiency": "beginner", "endorsements": 12, "duration_months": 14}, {"name": "AWS", "proficiency": "beginner", "endorsements": 15, "duration_months": 17}, {"name": "Embeddings", "proficiency": "advanced", "endorsements": 0, "duration_months": 12}, {"name": "Semantic Search", "proficiency": "advanced", "endorsements": 1, "duration_months": 10}, {"name": "LangChain", "proficiency": "advanced", "endorsements": 0, "duration_months": 10}, {"name": "Fine-tuning LLMs", "proficiency": "intermediate", "endorsements": 1, "duration_months": 6}, {"name": "Information Retrieval", "proficiency": "advanced", "endorsements": 0, "duration_months": 4}, {"name": "FAISS", "proficiency": "advanced", "endorsements": 1, "duration_months": 10}, {"name": "Pinecone", "proficiency": "intermediate", "endorsements": 3, "duration_months": 10}, {"name": "Prompt Engineering", "proficiency": "advanced", "endorsements": 2, "duration_months": 7}], "certifications": [{"name": "Six Sigma Green Belt", "issuer": "ASQ", "year": 2019}, {"name": "AWS Certified Cloud Practitioner", "issuer": "AWS", "year": 2018}], "languages": [{"language": "English", "proficiency": "professional"}, {"language": "Hindi", "proficiency": "native"}], "redrob_signals": {"profile_completeness_score": 34.6, "signup_date": "2023-08-08", "last_active_date": "2026-04-20", "open_to_work_flag": false, "profile_views_received_30d": 73, "applications_submitted_30d": 10, "recruiter_response_rate": 0.7, "avg_response_time_hours": 133.4, "skill_assessment_scores": {}, "connection_count": 452, "endorsements_received": 33, "notice_period_days": 30, "expected_salary_range_inr_lpa": {"min": 11.3, "max": 18.4}, "preferred_work_mode": "hybrid", "willing_to_relocate": false, "github_activity_score": -1, "search_appearance_30d": 120, "saved_by_recruiters_30d": 7, "interview_completion_rate": 0.46, "offer_acceptance_rate": -1, "verified_email": true, "verified_phone": false, "linkedin_connected": true}}
{"candidate_id": "CAND_0000210", "profile": {"anonymized_name": "Pooja Chopra", "headline": "Senior Data Engineer | Data pipelines & analytics", "summary": "Software / data professional with 6.3 years of experience building data pipelines, backend systems, and analytics infrastructure. Started my career in backend engineering and gradually moved closer to data \u2014 first dashboards, then ETL, now some basic ML. My toolkit is solid on the data engineering side \u2014 Python, SQL, Spark, Airflow, warehouse design \u2014 and I've completed a couple of self-directed ML projects (Kaggle competitions, side projects fine-tuning small models). Interested in transitioning toward more AI/ML-focused work, ideally at a company where I can leverage my existing data-infra skills while learning modern ML practice.", "location": "Berlin", "country": "Germany", "years_of_experience": 6.3, "current_title": "Senior Data Engineer", "current_company": "upGrad", "current_company_size": "1001-5000", "current_industry": "EdTech"}, "career_history": [{"company": "upGrad", "title": "Senior Data Engineer", "start_date": "2025-04-02", "end_date": null, "duration_months": 14, "is_current": true, "industry": "EdTech", "company_size": "1001-5000", "description": "Designed and maintained the analytical data warehouse on Snowflake supporting the BI team's ~50 dashboards. Wrote complex SQL \u2014 heavy on window functions, CTEs, and incremental modeling patterns via dbt. Worked on the data modeling side (dimensional modeling, slowly changing dimensions) as well as performance optimization (query tuning, cluster sizing, materialized views). Also built the lineage and documentation framework now in use across the data org."}, {"company": "Globex Inc", "title": "Backend Engineer", "start_date": "2021-12-19", "end_date": "2025-04-02", "duration_months": 40, "is_current": false, "industry": "Manufacturing", "company_size": "501-1000", "description": "Backend + data hybrid role at a growth-stage startup. Built the company's first proper data warehouse (migrating from a tangled set of Postgres replicas to a clean Snowflake setup with dbt), the orchestration layer (Airflow), and the BI integration (Looker). Shipped a couple of small predictive features but the bulk of the role was data infrastructure."}, {"company": "Razorpay", "title": "Data Engineer", "start_date": "2020-04-28", "end_date": "2021-12-19", "duration_months": 20, "is_current": false, "industry": "Fintech", "company_size": "1001-5000", "description": "Designed and maintained the analytical data warehouse on Snowflake supporting the BI team's ~50 dashboards. Wrote complex SQL \u2014 heavy on window functions, CTEs, and incremental modeling patterns via dbt. Worked on the data modeling side (dimensional modeling, slowly changing dimensions) as well as performance optimization (query tuning, cluster sizing, materialized views). Also built the lineage and documentation framework now in use across the data org."}], "education": [{"institution": "Generic State University", "degree": "B.Tech", "field_of_study": "Commerce", "start_year": 2016, "end_year": 2019, "grade": "8.48 CGPA", "tier": "tier_4"}], "skills": [{"name": "Computer Vision", "proficiency": "intermediate", "endorsements": 1, "duration_months": 12}, {"name": "Accounting", "proficiency": "beginner", "endorsements": 4, "duration_months": 18}, {"name": "GANs", "proficiency": "advanced", "endorsements": 5, "duration_months": 18}, {"name": "Object Detection", "proficiency": "intermediate", "endorsements": 5, "duration_months": 31}, {"name": "Angular", "proficiency": "intermediate", "endorsements": 7, "duration_months": 26}, {"name": "Speech Recognition", "proficiency": "intermediate", "endorsements": 12, "duration_months": 29}, {"name": "Learning to Rank", "proficiency": "intermediate", "endorsements": 5, "duration_months": 28}, {"name": "Statistical Modeling", "proficiency": "intermediate", "endorsements": 7, "duration_months": 10}, {"name": "LlamaIndex", "proficiency": "advanced", "endorsements": 53, "duration_months": 33}, {"name": "JavaScript", "proficiency": "beginner", "endorsements": 13, "duration_months": 14}, {"name": "LangChain", "proficiency": "advanced", "endorsements": 45, "duration_months": 48}], "certifications": [], "languages": [{"language": "English", "proficiency": "native"}, {"language": "Hindi", "proficiency": "conversational"}], "redrob_signals": {"profile_completeness_score": 66.5, "signup_date": "2026-02-13", "last_active_date": "2026-03-25", "open_to_work_flag": false, "profile_views_received_30d": 93, "applications_submitted_30d": 0, "recruiter_response_rate": 0.6, "avg_response_time_hours": 24.3, "skill_assessment_scores": {}, "connection_count": 942, "endorsements_received": 59, "notice_period_days": 120, "expected_salary_range_inr_lpa": {"min": 30.6, "max": 37.0}, "preferred_work_mode": "hybrid", "willing_to_relocate": false, "github_activity_score": 61.8, "search_appearance_30d": 30, "saved_by_recruiters_30d": 14, "interview_completion_rate": 0.53, "offer_acceptance_rate": 0.79, "verified_email": true, "verified_phone": true, "linkedin_connected": false}}
{"candidate_id": "CAND_0000211", "profile": {"anonymized_name": "Riya Singh", "headline": "Operations Manager | AI enthusiast | Building with LLMs", "summary": "Operations Manager with 5.0+ years of experience driving outcomes in my domain. I have built strong functional expertise in the typical responsibilities of the role, including team management, stakeholder communication, and project delivery. Recently I've been excited about how AI and GenAI tools can augment my work. I've been taking online courses on RAG and vector databases, experimenting with LangChain and the OpenAI API for side projects, and exploring how LLMs can streamline workflows in my current function. Open to roles that combine my existing domain experience with emerging AI technologies \u2014 I think the most interesting opportunities are at this intersection. Looking for positions where I can contribute both my functional expertise and grow my AI capabilities.", "location": "Kochi, Kerala", "country": "India", "years_of_experience": 5.0, "current_title": "Operations Manager", "current_company": "Infosys", "current_company_size": "10001+", "current_industry": "IT Services"}, "career_history": [{"company": "Infosys", "title": "Operations Manager", "start_date": "2024-05-07", "end_date": null, "duration_months": 25, "is_current": true, "industry": "IT Services", "company_size": "10001+", "description": "Content writing and SEO strategy for a tech-focused publication. Wrote longform articles on developer tools, cloud platforms, and AI/ML topics \u2014 including some that ranked on the first page of search for high-competition keywords. Managed a freelance writer pool and the editorial calendar. Recent work has been on AI-assisted content production, using LLM tools for research, drafting, and editing while maintaining editorial quality."}, {"company": "Wayne Enterprises", "title": "Sales Executive", "start_date": "2021-07-15", "end_date": "2024-04-30", "duration_months": 34, "is_current": false, "industry": "Conglomerate", "company_size": "10001+", "description": "Brand design and creative direction at a consumer-products company. Owned brand identity (logo, visual system, typography), packaging design, and digital creative across web and social. Led the recent rebrand and managed a small external agency for production work. 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{"candidate_id": "CAND_0000212", "profile": {"anonymized_name": "Pooja Mehta", "headline": "Customer Support | AI enthusiast | Building with LLMs", "summary": "Customer Support with 13.0+ years of experience driving outcomes in my domain. I have built strong functional expertise in the typical responsibilities of the role, including team management, stakeholder communication, and project delivery. Recently I've been excited about how AI and GenAI tools can augment my work. I've been taking online courses on RAG and vector databases, experimenting with LangChain and the OpenAI API for side projects, and exploring how LLMs can streamline workflows in my current function. Open to roles that combine my existing domain experience with emerging AI technologies \u2014 I think the most interesting opportunities are at this intersection. Looking for positions where I can contribute both my functional expertise and grow my AI capabilities.", "location": "Austin", "country": "USA", "years_of_experience": 13.0, "current_title": "Customer Support", "current_company": "Wayne Enterprises", "current_company_size": "10001+", "current_industry": "Conglomerate"}, "career_history": [{"company": "Wayne Enterprises", "title": "Customer Support", "start_date": "2024-12-03", "end_date": null, "duration_months": 18, "is_current": true, "industry": "Conglomerate", "company_size": "10001+", "description": "Enterprise sales of cloud software solutions into the mid-market segment. Carried a $1.8M ARR quota and consistently delivered against it across the last three years. Owned the full sales cycle: prospecting, discovery, technical evaluation (with SE support), commercial negotiation, and close. Strong on consultative selling for technical buyers; comfortable engaging with both engineering and finance stakeholders."}, {"company": "Pied Piper", "title": "HR Manager", "start_date": "2020-10-25", "end_date": "2024-12-03", "duration_months": 50, "is_current": false, "industry": "Software", "company_size": "11-50", "description": "Customer support team lead at a SaaS product. Managed a team of 8 support agents handling tier-1 and tier-2 tickets; owned the escalation process to engineering and the customer-feedback loop to product. Built out the support knowledge base and the agent training program. Strong on the people-management side and the process side; lighter on technical depth beyond product expertise."}, {"company": "Acme Corp", "title": "Business Analyst", "start_date": "2019-03-05", "end_date": "2020-08-26", "duration_months": 18, "is_current": false, "industry": "Manufacturing", "company_size": "201-500", "description": "Marketing leadership role at a B2B SaaS company. Owned the demand-generation function \u2014 content marketing, paid acquisition, SEO, email nurture. Built and managed a team of 5 across content, performance marketing, and marketing operations. Worked closely with sales on lead-quality definitions and the SDR-handoff process. Recent focus has been on account-based marketing for our enterprise segment."}, {"company": "Wayne Enterprises", "title": "Business Analyst", "start_date": "2015-10-22", "end_date": "2019-01-04", "duration_months": 39, "is_current": false, "industry": "Conglomerate", "company_size": "10001+", "description": "Brand design and creative direction at a consumer-products company. Owned brand identity (logo, visual system, typography), packaging design, and digital creative across web and social. Led the recent rebrand and managed a small external agency for production work. Comfortable across the Adobe suite, Figma, and the production side of brand and packaging design."}, {"company": "Stark Industries", "title": "Business Analyst", "start_date": "2013-04-21", "end_date": "2015-10-08", "duration_months": 30, "is_current": false, "industry": "Manufacturing", "company_size": "1001-5000", "description": "Enterprise sales of cloud software solutions into the mid-market segment. Carried a $1.8M ARR quota and consistently delivered against it across the last three years. Owned the full sales cycle: prospecting, discovery, technical evaluation (with SE support), commercial negotiation, and close. Strong on consultative selling for technical buyers; comfortable engaging with both engineering and finance stakeholders."}], "education": [{"institution": "Regional Technical Institute", "degree": "M.Sc", "field_of_study": "Data Science", "start_year": 2005, "end_year": 2010, "grade": "9.09 CGPA", "tier": "tier_4"}], "skills": [{"name": "Airflow", "proficiency": "intermediate", "endorsements": 15, "duration_months": 31}, {"name": "Scrum", "proficiency": "intermediate", "endorsements": 14, "duration_months": 25}, {"name": "Content Writing", "proficiency": "beginner", "endorsements": 4, "duration_months": 12}, {"name": "HTML", "proficiency": "beginner", "endorsements": 10, "duration_months": 4}, {"name": "Vue.js", "proficiency": "intermediate", "endorsements": 2, "duration_months": 27}, {"name": "Redux", "proficiency": "intermediate", "endorsements": 7, "duration_months": 8}, {"name": "Next.js", "proficiency": "beginner", "endorsements": 5, "duration_months": 13}, {"name": "Kubernetes", "proficiency": "intermediate", "endorsements": 5, "duration_months": 11}, {"name": "Marketing", "proficiency": "beginner", "endorsements": 13, "duration_months": 10}, {"name": "Go", "proficiency": "beginner", "endorsements": 4, "duration_months": 4}, {"name": "Fine-tuning LLMs", "proficiency": "advanced", "endorsements": 1, "duration_months": 6}, {"name": "Hugging Face Transformers", "proficiency": "intermediate", "endorsements": 4, "duration_months": 12}, {"name": "Prompt Engineering", "proficiency": "intermediate", "endorsements": 3, "duration_months": 8}, {"name": "Sentence Transformers", "proficiency": "advanced", "endorsements": 0, "duration_months": 6}, {"name": "FAISS", "proficiency": "intermediate", "endorsements": 0, "duration_months": 11}, {"name": "RAG", "proficiency": "intermediate", "endorsements": 3, "duration_months": 7}, {"name": "Pinecone", "proficiency": "intermediate", "endorsements": 4, "duration_months": 11}, {"name": "Information Retrieval", "proficiency": "intermediate", "endorsements": 3, "duration_months": 9}, {"name": "Semantic Search", "proficiency": "advanced", "endorsements": 3, "duration_months": 10}, {"name": "Embeddings", "proficiency": "intermediate", "endorsements": 4, "duration_months": 7}, {"name": "Recommendation Systems", "proficiency": "advanced", "endorsements": 4, "duration_months": 17}, {"name": "LLMs", "proficiency": "advanced", "endorsements": 3, "duration_months": 9}], "certifications": [], "languages": [{"language": "English", "proficiency": "professional"}, {"language": "Hindi", "proficiency": "native"}], "redrob_signals": {"profile_completeness_score": 35.7, "signup_date": "2024-07-06", "last_active_date": "2025-12-03", "open_to_work_flag": true, "profile_views_received_30d": 14, "applications_submitted_30d": 0, "recruiter_response_rate": 0.06, "avg_response_time_hours": 135.5, "skill_assessment_scores": {}, "connection_count": 42, "endorsements_received": 12, "notice_period_days": 30, "expected_salary_range_inr_lpa": {"min": 13.4, "max": 12.7}, "preferred_work_mode": "remote", "willing_to_relocate": false, "github_activity_score": -1, "search_appearance_30d": 180, "saved_by_recruiters_30d": 1, "interview_completion_rate": 0.33, "offer_acceptance_rate": 0.55, "verified_email": true, "verified_phone": false, "linkedin_connected": false}}
{"candidate_id": "CAND_0000220", "profile": {"anonymized_name": "Pranav Singh", "headline": "Marketing Manager | AI enthusiast | Building with LLMs", "summary": "Marketing Manager with 10.9+ years of experience driving outcomes in my domain. I have built strong functional expertise in the typical responsibilities of the role, including team management, stakeholder communication, and project delivery. Recently I've been excited about how AI and GenAI tools can augment my work. I've been taking online courses on RAG and vector databases, experimenting with LangChain and the OpenAI API for side projects, and exploring how LLMs can streamline workflows in my current function. Open to roles that combine my existing domain experience with emerging AI technologies \u2014 I think the most interesting opportunities are at this intersection. Looking for positions where I can contribute both my functional expertise and grow my AI capabilities.", "location": "Gurgaon, Haryana", "country": "India", "years_of_experience": 10.9, "current_title": "Marketing Manager", "current_company": "Dunder Mifflin", "current_company_size": "201-500", "current_industry": "Paper Products"}, "career_history": [{"company": "Dunder Mifflin", "title": "Marketing Manager", "start_date": "2023-11-09", "end_date": null, "duration_months": 31, "is_current": true, "industry": "Paper Products", "company_size": "201-500", "description": "Mechanical engineering design role at a hardware-product company. Led the design of two product subsystems through full lifecycle: concept, DFM/DFMA review, prototype, production tooling. Comfortable with CAD (SolidWorks, Creo), FEA (ANSYS), and the typical hardware-development cadence. Worked closely with manufacturing partners on production scale-up."}, {"company": "Infosys", "title": "Business Analyst", "start_date": "2022-09-08", "end_date": "2023-11-02", "duration_months": 14, "is_current": false, "industry": "IT Services", "company_size": "10001+", "description": "Mechanical engineering design role at a hardware-product company. Led the design of two product subsystems through full lifecycle: concept, DFM/DFMA review, prototype, production tooling. Comfortable with CAD (SolidWorks, Creo), FEA (ANSYS), and the typical hardware-development cadence. Worked closely with manufacturing partners on production scale-up."}, {"company": "Pied Piper", "title": "Project Manager", "start_date": "2020-01-22", "end_date": "2022-08-09", "duration_months": 31, "is_current": false, "industry": "Software", "company_size": "11-50", "description": "Content writing and SEO strategy for a tech-focused publication. Wrote longform articles on developer tools, cloud platforms, and AI/ML topics \u2014 including some that ranked on the first page of search for high-competition keywords. Managed a freelance writer pool and the editorial calendar. Recent work has been on AI-assisted content production, using LLM tools for research, drafting, and editing while maintaining editorial quality."}, {"company": "Wayne Enterprises", "title": "Operations Manager", "start_date": "2017-11-03", "end_date": "2020-01-22", "duration_months": 27, "is_current": false, "industry": "Conglomerate", "company_size": "10001+", "description": "Senior accounting role at a mid-sized company \u2014 month-end close, financial reporting, statutory compliance (GAAP / Ind-AS), and tax filings. Owned the GL, fixed-asset register, and the audit-readiness function. Managed a team of 3 staff accountants. Built strong process discipline around the close cycle, reducing close time from 12 days to 7 over the last two years."}, {"company": "Dunder Mifflin", "title": "Operations Manager", "start_date": "2015-07-17", "end_date": "2017-09-04", "duration_months": 26, "is_current": false, "industry": "Paper Products", "company_size": "201-500", "description": "Enterprise sales of cloud software solutions into the mid-market segment. Carried a $1.8M ARR quota and consistently delivered against it across the last three years. Owned the full sales cycle: prospecting, discovery, technical evaluation (with SE support), commercial negotiation, and close. Strong on consultative selling for technical buyers; comfortable engaging with both engineering and finance stakeholders."}], "education": [{"institution": "Tier-3 Engineering College", "degree": "M.E.", "field_of_study": "Statistics", "start_year": 2015, "end_year": 2018, "grade": "7.01 CGPA", "tier": "tier_4"}, {"institution": "Symbiosis International", "degree": "B.E.", "field_of_study": "Data Science", "start_year": 2017, "end_year": 2021, "grade": "8.04 CGPA", "tier": "tier_3"}], "skills": [{"name": "Sales", "proficiency": "beginner", "endorsements": 4, "duration_months": 16}, {"name": "Django", "proficiency": "beginner", "endorsements": 11, "duration_months": 12}, {"name": "Six Sigma", "proficiency": "beginner", "endorsements": 2, "duration_months": 10}, {"name": "Docker", "proficiency": "intermediate", "endorsements": 10, "duration_months": 20}, {"name": "Flask", "proficiency": "beginner", "endorsements": 8, "duration_months": 3}, {"name": "Next.js", "proficiency": "beginner", "endorsements": 14, "duration_months": 13}, {"name": "Kubeflow", "proficiency": "advanced", "endorsements": 0, "duration_months": 24}, {"name": "Prompt Engineering", "proficiency": "intermediate", "endorsements": 3, "duration_months": 7}, {"name": "Recommendation Systems", "proficiency": "intermediate", "endorsements": 1, "duration_months": 5}, {"name": "Hugging Face Transformers", "proficiency": "advanced", "endorsements": 3, "duration_months": 15}, {"name": "Embeddings", "proficiency": "intermediate", "endorsements": 0, "duration_months": 7}, {"name": "Information Retrieval", "proficiency": "advanced", "endorsements": 4, "duration_months": 13}, {"name": "Sentence Transformers", "proficiency": "advanced", "endorsements": 3, "duration_months": 10}, {"name": "Pinecone", "proficiency": "advanced", "endorsements": 4, "duration_months": 12}, {"name": "Semantic Search", "proficiency": "advanced", "endorsements": 2, "duration_months": 11}, {"name": "FAISS", "proficiency": "advanced", "endorsements": 1, "duration_months": 5}, {"name": "LangChain", "proficiency": "advanced", "endorsements": 0, "duration_months": 14}, {"name": "Vector Search", "proficiency": "advanced", "endorsements": 1, "duration_months": 4}], "certifications": [], "languages": [{"language": "English", "proficiency": "native"}, {"language": "Hindi", "proficiency": "conversational"}], "redrob_signals": {"profile_completeness_score": 29.6, "signup_date": "2025-09-11", "last_active_date": "2026-05-10", "open_to_work_flag": false, "profile_views_received_30d": 58, "applications_submitted_30d": 4, "recruiter_response_rate": 0.08, "avg_response_time_hours": 113.8, "skill_assessment_scores": {"Kubeflow": 48.9}, "connection_count": 273, "endorsements_received": 36, "notice_period_days": 60, "expected_salary_range_inr_lpa": {"min": 13.9, "max": 6.9}, "preferred_work_mode": "onsite", "willing_to_relocate": false, "github_activity_score": -1, "search_appearance_30d": 94, "saved_by_recruiters_30d": 6, "interview_completion_rate": 0.87, "offer_acceptance_rate": -1, "verified_email": true, "verified_phone": false, "linkedin_connected": false}}
{"candidate_id": "CAND_0000256", "profile": {"anonymized_name": "Siya Banerjee", "headline": "Java Developer | Full-stack development", "summary": "Software engineer with 2.3 years of experience across web, backend, and cloud systems. Strong fundamentals in software development and system design. I've worked across web frontends, REST APIs, and cloud deployments; comfortable in most parts of a typical SaaS stack. I've been keeping up with AI/ML at a self-learner level \u2014 taken some online courses, played with the OpenAI and Anthropic APIs, built a small RAG side project \u2014 but I haven't done it in a professional capacity yet. Open to roles where I can either deepen my software engineering work or, if the team is open to it, start contributing to ML-adjacent systems.", "location": "Ahmedabad, Gujarat", "country": "India", "years_of_experience": 2.3, "current_title": "Java Developer", "current_company": "Tech Mahindra", "current_company_size": "10001+", "current_industry": "IT Services"}, "career_history": [{"company": "Tech Mahindra", "title": "Java Developer", "start_date": "2024-03-08", "end_date": null, "duration_months": 27, "is_current": true, "industry": "IT Services", "company_size": "10001+", "description": "Test automation and QA engineering for a fintech product. Built and maintained the end-to-end test suite using Selenium and pytest, plus the load-testing setup using Locust. Worked closely with developers on testability patterns and with product on acceptance criteria. Recent work has been on shifting test responsibility into the dev team \u2014 moving from QA-as-gate to QA-as-coach. Career has been entirely in QA/test engineering."}], "education": [{"institution": "Christ University", "degree": "B.Tech", "field_of_study": "Electronics", "start_year": 2006, "end_year": 2011, "grade": "77%", "tier": "tier_3"}, {"institution": "Generic State University", "degree": "Ph.D", "field_of_study": "Commerce", "start_year": 2019, "end_year": 2022, "grade": "6.56 CGPA", "tier": "tier_4"}], "skills": [{"name": "Information Retrieval", "proficiency": "intermediate", "endorsements": 6, "duration_months": 28}, {"name": "GraphQL", "proficiency": "intermediate", "endorsements": 1, "duration_months": 11}, {"name": "CSS", "proficiency": "intermediate", "endorsements": 0, "duration_months": 11}, {"name": "YOLO", "proficiency": "advanced", "endorsements": 6, "duration_months": 44}, {"name": "scikit-learn", "proficiency": "intermediate", "endorsements": 8, "duration_months": 25}, {"name": "MLOps", "proficiency": "advanced", "endorsements": 24, "duration_months": 22}, {"name": "CNN", "proficiency": "advanced", "endorsements": 13, "duration_months": 46}, {"name": "Machine Learning", "proficiency": "intermediate", "endorsements": 15, "duration_months": 16}, {"name": "GANs", "proficiency": "intermediate", "endorsements": 10, "duration_months": 24}, {"name": "Semantic Search", "proficiency": "advanced", "endorsements": 4, "duration_months": 19}, {"name": "Weights & Biases", "proficiency": "advanced", "endorsements": 44, "duration_months": 59}, {"name": "Reinforcement Learning", "proficiency": "intermediate", "endorsements": 5, "duration_months": 36}, {"name": "Computer Vision", "proficiency": "intermediate", "endorsements": 3, "duration_months": 22}], "certifications": [], "languages": [{"language": "English", "proficiency": "native"}, {"language": "Hindi", "proficiency": "conversational"}], "redrob_signals": {"profile_completeness_score": 53.9, "signup_date": "2023-10-17", "last_active_date": "2025-12-12", "open_to_work_flag": false, "profile_views_received_30d": 115, "applications_submitted_30d": 10, "recruiter_response_rate": 0.15, "avg_response_time_hours": 215.0, "skill_assessment_scores": {}, "connection_count": 452, "endorsements_received": 2, "notice_period_days": 120, "expected_salary_range_inr_lpa": {"min": 7.4, "max": 30.6}, "preferred_work_mode": "remote", "willing_to_relocate": false, "github_activity_score": -1, "search_appearance_30d": 257, "saved_by_recruiters_30d": 2, "interview_completion_rate": 0.61, "offer_acceptance_rate": 0.48, "verified_email": true, "verified_phone": false, "linkedin_connected": true}}
{"candidate_id": "CAND_0000312", "profile": {"anonymized_name": "Aarohi Sen", "headline": "Content Writer | Exploring AI & GenAI applications", "summary": "Content Writer with 11.5+ years of experience driving outcomes in my domain. I have built strong functional expertise in the typical responsibilities of the role, including team management, stakeholder communication, and project delivery. Recently I've been excited about how AI and GenAI tools can augment my work. I've been taking online courses on RAG and vector databases, experimenting with LangChain and the OpenAI API for side projects, and exploring how LLMs can streamline workflows in my current function. Open to roles that combine my existing domain experience with emerging AI technologies \u2014 I think the most interesting opportunities are at this intersection. Looking for positions where I can contribute both my functional expertise and grow my AI capabilities.", "location": "Delhi, Delhi", "country": "India", "years_of_experience": 11.5, "current_title": "Content Writer", "current_company": "TCS", "current_company_size": "10001+", "current_industry": "IT Services"}, "career_history": [{"company": "TCS", "title": "Content Writer", "start_date": "2023-09-10", "end_date": null, "duration_months": 33, "is_current": true, "industry": "IT Services", "company_size": "10001+", "description": "Senior accounting role at a mid-sized company \u2014 month-end close, financial reporting, statutory compliance (GAAP / Ind-AS), and tax filings. Owned the GL, fixed-asset register, and the audit-readiness function. Managed a team of 3 staff accountants. Built strong process discipline around the close cycle, reducing close time from 12 days to 7 over the last two years."}, {"company": "TCS", "title": "Customer Support", "start_date": "2020-03-15", "end_date": "2023-08-27", "duration_months": 42, "is_current": false, "industry": "IT Services", "company_size": "10001+", "description": "Operations management role at a logistics company. Owned daily fulfillment operations across 3 warehouses, managing a team of 80 across receiving, picking, packing, and outbound. Built and tracked the operational KPIs (on-time fulfillment, accuracy, cost per order) and led the continuous improvement initiatives that drove a 22% productivity gain over 18 months."}, {"company": "Initech", "title": "Mechanical Engineer", "start_date": "2016-06-04", "end_date": "2020-03-15", "duration_months": 46, "is_current": false, "industry": "Software", "company_size": "51-200", "description": "Enterprise sales of cloud software solutions into the mid-market segment. Carried a $1.8M ARR quota and consistently delivered against it across the last three years. Owned the full sales cycle: prospecting, discovery, technical evaluation (with SE support), commercial negotiation, and close. Strong on consultative selling for technical buyers; comfortable engaging with both engineering and finance stakeholders."}, {"company": "Acme Corp", "title": "HR Manager", "start_date": "2015-03-05", "end_date": "2016-05-28", "duration_months": 15, "is_current": false, "industry": "Manufacturing", "company_size": "201-500", "description": "Senior accounting role at a mid-sized company \u2014 month-end close, financial reporting, statutory compliance (GAAP / Ind-AS), and tax filings. Owned the GL, fixed-asset register, and the audit-readiness function. Managed a team of 3 staff accountants. 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{"candidate_id": "CAND_0000322", "profile": {"anonymized_name": "Ayaan Mehta", "headline": "HR Manager | Exploring AI & GenAI applications", "summary": "HR Manager with 12.0+ years of experience driving outcomes in my domain. I have built strong functional expertise in the typical responsibilities of the role, including team management, stakeholder communication, and project delivery. Recently I've been excited about how AI and GenAI tools can augment my work. I've been taking online courses on RAG and vector databases, experimenting with LangChain and the OpenAI API for side projects, and exploring how LLMs can streamline workflows in my current function. Open to roles that combine my existing domain experience with emerging AI technologies \u2014 I think the most interesting opportunities are at this intersection. Looking for positions where I can contribute both my functional expertise and grow my AI capabilities.", "location": "Ahmedabad, Gujarat", "country": "India", "years_of_experience": 12.0, "current_title": "HR Manager", "current_company": "Acme Corp", "current_company_size": "201-500", "current_industry": "Manufacturing"}, "career_history": [{"company": "Acme Corp", "title": "HR Manager", "start_date": "2021-12-19", "end_date": null, "duration_months": 54, "is_current": true, "industry": "Manufacturing", "company_size": "201-500", "description": "Mechanical engineering design role at a hardware-product company. Led the design of two product subsystems through full lifecycle: concept, DFM/DFMA review, prototype, production tooling. Comfortable with CAD (SolidWorks, Creo), FEA (ANSYS), and the typical hardware-development cadence. 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{"candidate_id": "CAND_0000330", "profile": {"anonymized_name": "Riya Joshi", "headline": "Civil Engineer | Exploring AI & GenAI applications", "summary": "Civil Engineer with 12.0+ years of experience driving outcomes in my domain. I have built strong functional expertise in the typical responsibilities of the role, including team management, stakeholder communication, and project delivery. Recently I've been excited about how AI and GenAI tools can augment my work. I've been taking online courses on RAG and vector databases, experimenting with LangChain and the OpenAI API for side projects, and exploring how LLMs can streamline workflows in my current function. Open to roles that combine my existing domain experience with emerging AI technologies \u2014 I think the most interesting opportunities are at this intersection. 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Worked closely with manufacturing partners on production scale-up."}, {"company": "Hooli", "title": "Operations Manager", "start_date": "2019-07-03", "end_date": "2022-03-19", "duration_months": 33, "is_current": false, "industry": "Software", "company_size": "1001-5000", "description": "Senior accounting role at a mid-sized company \u2014 month-end close, financial reporting, statutory compliance (GAAP / Ind-AS), and tax filings. Owned the GL, fixed-asset register, and the audit-readiness function. Managed a team of 3 staff accountants. Built strong process discipline around the close cycle, reducing close time from 12 days to 7 over the last two years."}, {"company": "Acme Corp", "title": "Graphic Designer", "start_date": "2017-01-14", "end_date": "2019-07-03", "duration_months": 30, "is_current": false, "industry": "Manufacturing", "company_size": "201-500", "description": "Operations management role at a logistics company. Owned daily fulfillment operations across 3 warehouses, managing a team of 80 across receiving, picking, packing, and outbound. Built and tracked the operational KPIs (on-time fulfillment, accuracy, cost per order) and led the continuous improvement initiatives that drove a 22% productivity gain over 18 months."}, {"company": "Wayne Enterprises", "title": "Customer Support", "start_date": "2014-09-13", "end_date": "2016-12-31", "duration_months": 28, "is_current": false, "industry": "Conglomerate", "company_size": "10001+", "description": "Mechanical engineering design role at a hardware-product company. Led the design of two product subsystems through full lifecycle: concept, DFM/DFMA review, prototype, production tooling. Comfortable with CAD (SolidWorks, Creo), FEA (ANSYS), and the typical hardware-development cadence. 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{"candidate_id": "CAND_0000344", "profile": {"anonymized_name": "Ayaan Mittal", "headline": "Business Analyst | AI enthusiast | Building with LLMs", "summary": "Business Analyst with 2.5+ years of experience driving outcomes in my domain. I have built strong functional expertise in the typical responsibilities of the role, including team management, stakeholder communication, and project delivery. Recently I've been excited about how AI and GenAI tools can augment my work. I've been taking online courses on RAG and vector databases, experimenting with LangChain and the OpenAI API for side projects, and exploring how LLMs can streamline workflows in my current function. Open to roles that combine my existing domain experience with emerging AI technologies \u2014 I think the most interesting opportunities are at this intersection. Looking for positions where I can contribute both my functional expertise and grow my AI capabilities.", "location": "Bangalore, Karnataka", "country": "India", "years_of_experience": 2.5, "current_title": "Business Analyst", "current_company": "Hooli", "current_company_size": "1001-5000", "current_industry": "Software"}, "career_history": [{"company": "Hooli", "title": "Business Analyst", "start_date": "2024-12-03", "end_date": null, "duration_months": 18, "is_current": true, "industry": "Software", "company_size": "1001-5000", "description": "Mechanical engineering design role at a hardware-product company. Led the design of two product subsystems through full lifecycle: concept, DFM/DFMA review, prototype, production tooling. Comfortable with CAD (SolidWorks, Creo), FEA (ANSYS), and the typical hardware-development cadence. Worked closely with manufacturing partners on production scale-up."}, {"company": "Wayne Enterprises", "title": "Graphic Designer", "start_date": "2023-12-09", "end_date": "2024-12-03", "duration_months": 12, "is_current": false, "industry": "Conglomerate", "company_size": "10001+", "description": "Brand design and creative direction at a consumer-products company. Owned brand identity (logo, visual system, typography), packaging design, and digital creative across web and social. Led the recent rebrand and managed a small external agency for production work. Comfortable across the Adobe suite, Figma, and the production side of brand and packaging design."}], "education": [{"institution": "Lovely Professional University", "degree": "M.Sc", "field_of_study": "Data Science", "start_year": 2007, "end_year": 2012, "grade": "7.76 CGPA", "tier": "tier_3"}], "skills": [{"name": "Azure", "proficiency": "intermediate", "endorsements": 6, "duration_months": 33}, {"name": "Figma", "proficiency": "intermediate", "endorsements": 6, "duration_months": 36}, {"name": "Django", "proficiency": "intermediate", "endorsements": 3, "duration_months": 11}, {"name": "ETL", "proficiency": "beginner", "endorsements": 2, "duration_months": 9}, {"name": "Docker", "proficiency": "beginner", "endorsements": 10, "duration_months": 18}, {"name": "Content Writing", "proficiency": "beginner", "endorsements": 6, "duration_months": 11}, {"name": "Next.js", "proficiency": "intermediate", "endorsements": 2, "duration_months": 29}, {"name": "Fine-tuning LLMs", "proficiency": "advanced", "endorsements": 4, "duration_months": 12}, {"name": "RAG", "proficiency": "advanced", "endorsements": 3, "duration_months": 10}, {"name": "Hugging Face Transformers", "proficiency": "advanced", "endorsements": 2, "duration_months": 16}, {"name": "LangChain", "proficiency": "advanced", "endorsements": 4, "duration_months": 9}, {"name": "Recommendation Systems", "proficiency": "advanced", "endorsements": 2, "duration_months": 6}, {"name": "Semantic Search", "proficiency": "intermediate", "endorsements": 1, "duration_months": 5}, {"name": "Prompt Engineering", "proficiency": "advanced", "endorsements": 0, "duration_months": 9}, {"name": "Information Retrieval", "proficiency": "advanced", "endorsements": 4, "duration_months": 13}, {"name": "LLMs", "proficiency": "advanced", "endorsements": 1, "duration_months": 10}, {"name": "Vector Search", "proficiency": "advanced", "endorsements": 2, "duration_months": 9}], "certifications": [{"name": "Scrum Master Certified", "issuer": "Scrum Alliance", "year": 2025}], "languages": [{"language": "English", "proficiency": "professional"}, {"language": "Hindi", "proficiency": "native"}], "redrob_signals": {"profile_completeness_score": 75.2, "signup_date": "2025-11-14", "last_active_date": "2026-02-24", "open_to_work_flag": false, "profile_views_received_30d": 3, "applications_submitted_30d": 9, "recruiter_response_rate": 0.58, "avg_response_time_hours": 96.2, "skill_assessment_scores": {}, "connection_count": 586, "endorsements_received": 47, "notice_period_days": 150, "expected_salary_range_inr_lpa": {"min": 9.1, "max": 6.4}, "preferred_work_mode": "flexible", "willing_to_relocate": true, "github_activity_score": 39.6, "search_appearance_30d": 120, "saved_by_recruiters_30d": 6, "interview_completion_rate": 0.59, "offer_acceptance_rate": -1, "verified_email": true, "verified_phone": false, "linkedin_connected": false}}
{"candidate_id": "CAND_0001610", "profile": {"anonymized_name": "Aryan Banerjee", "headline": "Machine Learning Engineer | Applied ML | Building intelligent products", "summary": "Machine learning engineer with 5.2 years of experience building ML-powered features in production. Strong background in NLP, recommendation systems, and applied AI; comfortable across the ML stack from feature engineering through deployment. Recently, I built our semantic search infrastructure from scratch \u2014 sentence-transformers, FAISS, the works. I've spent enough time debugging production ranking issues to know which signals matter and which are noise. My academic background is in CS/ML but my main learning has come from shipping real systems and seeing what holds up under production load. Open to senior IC roles in applied ML or AI engineering, ideally at product companies where I'd own a meaningful piece of the ML stack.", "location": "Trivandrum, Kerala", "country": "India", "years_of_experience": 3.0, "current_title": "Machine Learning Engineer", "current_company": "Dream11", "current_company_size": "1001-5000", "current_industry": "Gaming"}, "career_history": [{"company": "Dream11", "title": "Machine Learning Engineer", "start_date": "2023-11-09", "end_date": null, "duration_months": 31, "is_current": true, "industry": "Gaming", "company_size": "1001-5000", "description": "Developed a semantic search feature for an internal knowledge base of ~500K documents. Used sentence-transformers (all-MiniLM-L6-v2 initially, later upgraded to bge-base) with FAISS for fast nearest-neighbor retrieval. Designed the query expansion module that handles vocabulary mismatch between user queries and document terms. Reported search-relevance improvement of 35% over the prior Elasticsearch BM25 setup, validated through human relevance judgments."}, {"company": "Flipkart", "title": "Senior Data Scientist", "start_date": "2022-02-17", "end_date": "2023-11-09", "duration_months": 21, "is_current": false, "industry": "E-commerce", "company_size": "10001+", "description": "Built a content recommendation system serving 10M+ users that combined collaborative filtering with content-based ranking. The system uses item-item similarity (via sentence-transformer embeddings) for cold starts and a gradient-boosted model trained on engagement signals for warm users. Most of my time went into the feature pipeline (~200 features) and the A/B testing infrastructure. The launch improved 7-day retention by 6% and time spent per session by 14%."}, {"company": "Zoho", "title": "Machine Learning Engineer", "start_date": "2021-05-23", "end_date": "2022-02-17", "duration_months": 9, "is_current": false, "industry": "SaaS", "company_size": "10001+", "description": "Trained and shipped multiple ranking models for our product's discovery feed using XGBoost and LightGBM. Designed features across three families: content metadata, user behavior signals, and item engagement history. Owned the offline-online correlation analysis that determined which offline metrics actually predicted A/B test outcomes. Worked closely with PMs to define the optimization target (click-through vs. dwell time vs. downstream conversion) \u2014 that work was as important as the modeling itself."}], "education": [{"institution": "Manipal Institute of Technology", "degree": "B.Sc", "field_of_study": "Computer Engineering", "start_year": 2005, "end_year": 2009, "grade": "7.21 CGPA", "tier": "tier_2"}], "skills": [{"name": "Speech Recognition", "proficiency": "advanced", "endorsements": 51, "duration_months": 50}, {"name": "Haystack", "proficiency": "expert", "endorsements": 34, "duration_months": 67}, {"name": "Deep Learning", "proficiency": "expert", "endorsements": 3, "duration_months": 84}, {"name": "Kubeflow", "proficiency": "advanced", "endorsements": 45, "duration_months": 45}, {"name": "Pinecone", "proficiency": "expert", "endorsements": 16, "duration_months": 72}, {"name": "Salesforce CRM", "proficiency": "beginner", "endorsements": 7, "duration_months": 11}, {"name": "Feature Engineering", "proficiency": "advanced", "endorsements": 36, "duration_months": 53}, {"name": "scikit-learn", "proficiency": "expert", "endorsements": 60, "duration_months": 64}, {"name": "Fine-tuning LLMs", "proficiency": "expert", "endorsements": 18, "duration_months": 92}, {"name": "Data Science", "proficiency": "intermediate", "endorsements": 6, "duration_months": 10}, {"name": "Milvus", "proficiency": "expert", "endorsements": 17, "duration_months": 51}, {"name": "Sentence Transformers", "proficiency": "expert", "endorsements": 7, "duration_months": 59}, {"name": "Reinforcement Learning", "proficiency": "intermediate", "endorsements": 6, "duration_months": 23}, {"name": "Information Retrieval", "proficiency": "advanced", "endorsements": 45, "duration_months": 34}, {"name": "NLP", "proficiency": "advanced", "endorsements": 50, "duration_months": 29}, {"name": "PyTorch", "proficiency": "expert", "endorsements": 39, "duration_months": 61}, {"name": "Computer Vision", "proficiency": "intermediate", "endorsements": 1, "duration_months": 28}, {"name": "FAISS", "proficiency": "expert", "endorsements": 39, "duration_months": 85}], "certifications": [], "languages": [{"language": "English", "proficiency": "professional"}, {"language": "Hindi", "proficiency": "conversational"}], "redrob_signals": {"profile_completeness_score": 98.0, "signup_date": "2025-09-09", "last_active_date": "2026-04-12", "open_to_work_flag": true, "profile_views_received_30d": 188, "applications_submitted_30d": 21, "recruiter_response_rate": 0.57, "avg_response_time_hours": 38.6, "skill_assessment_scores": {}, "connection_count": 1027, "endorsements_received": 97, "notice_period_days": 90, "expected_salary_range_inr_lpa": {"min": 26.3, "max": 41.7}, "preferred_work_mode": "hybrid", "willing_to_relocate": true, "github_activity_score": 40.0, "search_appearance_30d": 306, "saved_by_recruiters_30d": 53, "interview_completion_rate": 0.7, "offer_acceptance_rate": 0.78, "verified_email": true, "verified_phone": false, "linkedin_connected": true}}
{"candidate_id": "CAND_0003582", "profile": {"anonymized_name": "Ishaan Tiwari", "headline": "Mobile Developer | Cloud & DevOps", "summary": "Software engineer with 8.2 years of experience across web, backend, and cloud systems. Strong fundamentals in software development and system design. My background is full-stack, but my comfort zone is the backend and the database. I've been keeping up with AI/ML at a self-learner level \u2014 taken some online courses, played with the OpenAI and Anthropic APIs, built a small RAG side project \u2014 but I haven't done it in a professional capacity yet. Open to roles where I can either deepen my software engineering work or, if the team is open to it, start contributing to ML-adjacent systems.", "location": "Kolkata, West Bengal", "country": "India", "years_of_experience": 8.2, "current_title": "Mobile Developer", "current_company": "Mphasis", "current_company_size": "10001+", "current_industry": "IT Services"}, "career_history": [{"company": "Mphasis", "title": "Mobile Developer", "start_date": "2024-11-03", "end_date": null, "duration_months": 19, "is_current": true, "industry": "IT Services", "company_size": "10001+", "description": "Test automation and QA engineering for a fintech product. Built and maintained the end-to-end test suite using Selenium and pytest, plus the load-testing setup using Locust. Worked closely with developers on testability patterns and with product on acceptance criteria. Recent work has been on shifting test responsibility into the dev team \u2014 moving from QA-as-gate to QA-as-coach. Career has been entirely in QA/test engineering."}, {"company": "Wayne Enterprises", "title": "DevOps Engineer", "start_date": "2022-11-07", "end_date": "2024-10-27", "duration_months": 24, "is_current": false, "industry": "Conglomerate", "company_size": "10001+", "description": "Frontend engineering at a media company. React, TypeScript, and the typical surrounding tooling (Webpack, Jest, Cypress). Built the company's design system from scratch and led the migration from a legacy AngularJS app. Strong on the frontend craft \u2014 accessibility, performance, animations \u2014 but limited backend exposure."}, {"company": "Initech", "title": ".NET Developer", "start_date": "2018-05-02", "end_date": "2022-11-07", "duration_months": 55, "is_current": false, "industry": "Software", "company_size": "51-200", "description": "Cloud infrastructure and DevOps work at an enterprise SaaS company. Owned the AWS account architecture (VPC, IAM, networking), the Terraform modules for our service deployments, and the Kubernetes cluster operations. Designed the CI/CD pipelines (GitLab CI + ArgoCD) and the monitoring stack (Prometheus, Grafana, Loki). Strong on the infra and ops side; haven't done much application development."}], "education": [{"institution": "Anna University", "degree": "Ph.D", "field_of_study": "MBA", "start_year": 2013, "end_year": 2017, "grade": "9.07 CGPA", "tier": "tier_2"}], "skills": [{"name": "Docker", "proficiency": "beginner", "endorsements": 0, "duration_months": 2}, {"name": "Image Classification", "proficiency": "intermediate", "endorsements": 9, "duration_months": 35}, {"name": "MLflow", "proficiency": "expert", "endorsements": 2, "duration_months": 0}, {"name": "Photoshop", "proficiency": "expert", "endorsements": 2, "duration_months": 0}, {"name": "TTS", "proficiency": "advanced", "endorsements": 16, "duration_months": 51}, {"name": "Spring Boot", "proficiency": "beginner", "endorsements": 10, "duration_months": 3}, {"name": "JavaScript", "proficiency": "beginner", "endorsements": 9, "duration_months": 10}, {"name": "Content Writing", "proficiency": "expert", "endorsements": 0, "duration_months": 0}], "certifications": [], "languages": [{"language": "English", "proficiency": "professional"}, {"language": "Hindi", "proficiency": "professional"}], "redrob_signals": {"profile_completeness_score": 65.1, "signup_date": "2023-10-07", "last_active_date": "2026-02-10", "open_to_work_flag": false, "profile_views_received_30d": 65, "applications_submitted_30d": 9, "recruiter_response_rate": 0.29, "avg_response_time_hours": 167.7, "skill_assessment_scores": {"TTS": 37.2}, "connection_count": 341, "endorsements_received": 72, "notice_period_days": 120, "expected_salary_range_inr_lpa": {"min": 18.7, "max": 31.1}, "preferred_work_mode": "flexible", "willing_to_relocate": true, "github_activity_score": -1, "search_appearance_30d": 251, "saved_by_recruiters_30d": 2, "interview_completion_rate": 0.86, "offer_acceptance_rate": 0.21, "verified_email": true, "verified_phone": false, "linkedin_connected": true}}
{"candidate_id": "CAND_0007353", "profile": {"anonymized_name": "Aarav Subramanian", "headline": "Frontend Engineer | Full-stack development", "summary": "Software engineer with 9.9 years of experience across web, backend, and cloud systems. Strong fundamentals in software development and system design. I've worked across web frontends, REST APIs, and cloud deployments; comfortable in most parts of a typical SaaS stack. I've been keeping up with AI/ML at a self-learner level \u2014 taken some online courses, played with the OpenAI and Anthropic APIs, built a small RAG side project \u2014 but I haven't done it in a professional capacity yet. Open to roles where I can either deepen my software engineering work or, if the team is open to it, start contributing to ML-adjacent systems.", "location": "Noida, Uttar Pradesh", "country": "India", "years_of_experience": 9.9, "current_title": "Frontend Engineer", "current_company": "Wayne Enterprises", "current_company_size": "10001+", "current_industry": "Conglomerate"}, "career_history": [{"company": "Wayne Enterprises", "title": "Frontend Engineer", "start_date": "2023-09-10", "end_date": null, "duration_months": 166, "is_current": true, "industry": "Conglomerate", "company_size": "10001+", "description": "Frontend engineering at a media company. React, TypeScript, and the typical surrounding tooling (Webpack, Jest, Cypress). Built the company's design system from scratch and led the migration from a legacy AngularJS app. Strong on the frontend craft \u2014 accessibility, performance, animations \u2014 but limited backend exposure."}, {"company": "Globex Inc", "title": "Full Stack Developer", "start_date": "2021-08-21", "end_date": "2023-09-10", "duration_months": 25, "is_current": false, "industry": "Manufacturing", "company_size": "501-1000", "description": "Full-stack web application development at a SaaS company. Built React-based admin interfaces and the Node.js REST API backing them. Worked across the stack: frontend components, REST endpoint design, PostgreSQL schema, deployment via Docker/Kubernetes. Comfortable in most parts of a typical web stack though my comfort zone is the backend and database side. Recent learning has been on the testing and CI/CD discipline."}, {"company": "Hooli", "title": "DevOps Engineer", "start_date": "2017-09-11", "end_date": "2021-08-21", "duration_months": 48, "is_current": false, "industry": "Software", "company_size": "1001-5000", "description": "Cloud infrastructure and DevOps work at an enterprise SaaS company. Owned the AWS account architecture (VPC, IAM, networking), the Terraform modules for our service deployments, and the Kubernetes cluster operations. Designed the CI/CD pipelines (GitLab CI + ArgoCD) and the monitoring stack (Prometheus, Grafana, Loki). Strong on the infra and ops side; haven't done much application development."}, {"company": "Stark Industries", "title": "Full Stack Developer", "start_date": "2016-09-16", "end_date": "2017-09-11", "duration_months": 12, "is_current": false, "industry": "Manufacturing", "company_size": "1001-5000", "description": "Frontend engineering at a media company. React, TypeScript, and the typical surrounding tooling (Webpack, Jest, Cypress). Built the company's design system from scratch and led the migration from a legacy AngularJS app. Strong on the frontend craft \u2014 accessibility, performance, animations \u2014 but limited backend exposure."}], "education": [{"institution": "Anna University", "degree": "M.Tech", "field_of_study": "Information Technology", "start_year": 2006, "end_year": 2010, "grade": "7.86 CGPA", "tier": "tier_2"}], "skills": [{"name": "Tailwind", "proficiency": "intermediate", "endorsements": 8, "duration_months": 12}, {"name": "Apache Flink", "proficiency": "beginner", "endorsements": 8, "duration_months": 16}, {"name": "Content Writing", "proficiency": "intermediate", "endorsements": 11, "duration_months": 19}, {"name": "Hadoop", "proficiency": "intermediate", "endorsements": 1, "duration_months": 22}, {"name": "RAG", "proficiency": "advanced", "endorsements": 7, "duration_months": 23}, {"name": "Reinforcement Learning", "proficiency": "intermediate", "endorsements": 6, "duration_months": 12}, {"name": "Microservices", "proficiency": "intermediate", "endorsements": 13, "duration_months": 32}], "certifications": [{"name": "AWS Certified Cloud Practitioner", "issuer": "AWS", "year": 2021}], "languages": [{"language": "English", "proficiency": "native"}, {"language": "Hindi", "proficiency": "conversational"}], "redrob_signals": {"profile_completeness_score": 78.9, "signup_date": "2024-03-13", "last_active_date": "2026-03-21", "open_to_work_flag": false, "profile_views_received_30d": 61, "applications_submitted_30d": 8, "recruiter_response_rate": 0.65, "avg_response_time_hours": 177.2, "skill_assessment_scores": {"RAG": 34.0}, "connection_count": 574, "endorsements_received": 14, "notice_period_days": 120, "expected_salary_range_inr_lpa": {"min": 16.0, "max": 14.3}, "preferred_work_mode": "onsite", "willing_to_relocate": false, "github_activity_score": 37.3, "search_appearance_30d": 119, "saved_by_recruiters_30d": 9, "interview_completion_rate": 0.42, "offer_acceptance_rate": 0.37, "verified_email": false, "verified_phone": false, "linkedin_connected": true}}
{"candidate_id": "CAND_0008960", "profile": {"anonymized_name": "Meera Naidu", "headline": "Graphic Designer | Helping teams scale", "summary": "Professional with 10.3+ years of experience. My professional background is in marketing manager \u2014 I've built and led teams, owned KPIs, and driven business outcomes in this domain. Lately I've been curious about how AI tools could augment my work \u2014 I've experimented with ChatGPT and a few other tools for productivity and content creation, and I think the space is exciting. Open to roles where I can apply my domain expertise alongside emerging AI capabilities.", "location": "Mumbai, Maharashtra", "country": "India", "years_of_experience": 10.3, "current_title": "Graphic Designer", "current_company": "Stark Industries", "current_company_size": "1001-5000", "current_industry": "Manufacturing"}, "career_history": [{"company": "Stark Industries", "title": "Graphic Designer", "start_date": "2024-09-04", "end_date": null, "duration_months": 171, "is_current": true, "industry": "Manufacturing", "company_size": "1001-5000", "description": "Senior accounting role at a mid-sized company \u2014 month-end close, financial reporting, statutory compliance (GAAP / Ind-AS), and tax filings. Owned the GL, fixed-asset register, and the audit-readiness function. Managed a team of 3 staff accountants. Built strong process discipline around the close cycle, reducing close time from 12 days to 7 over the last two years."}, {"company": "Hooli", "title": "Customer Support", "start_date": "2020-11-24", "end_date": "2024-09-04", "duration_months": 46, "is_current": false, "industry": "Software", "company_size": "1001-5000", "description": "Enterprise sales of cloud software solutions into the mid-market segment. Carried a $1.8M ARR quota and consistently delivered against it across the last three years. Owned the full sales cycle: prospecting, discovery, technical evaluation (with SE support), commercial negotiation, and close. Strong on consultative selling for technical buyers; comfortable engaging with both engineering and finance stakeholders."}, {"company": "TCS", "title": "Customer Support", "start_date": "2018-09-06", "end_date": "2020-11-24", "duration_months": 27, "is_current": false, "industry": "IT Services", "company_size": "10001+", "description": "Business analyst at a consulting firm, working primarily with retail and CPG clients. Conducted business diagnostics, process re-engineering work, and digital transformation strategy projects. Strong on stakeholder management, structured problem-solving, and the typical consulting toolkit (slide-craft, Excel modeling, executive communication). Recent project work involved AI-strategy advisory but my own technical depth in AI is limited."}, {"company": "Infosys", "title": "Project Manager", "start_date": "2016-06-18", "end_date": "2018-09-06", "duration_months": 27, "is_current": false, "industry": "IT Services", "company_size": "10001+", "description": "Enterprise sales of cloud software solutions into the mid-market segment. Carried a $1.8M ARR quota and consistently delivered against it across the last three years. Owned the full sales cycle: prospecting, discovery, technical evaluation (with SE support), commercial negotiation, and close. Strong on consultative selling for technical buyers; comfortable engaging with both engineering and finance stakeholders."}], "education": [{"institution": "Symbiosis International", "degree": "M.Tech", "field_of_study": "Commerce", "start_year": 2017, "end_year": 2022, "grade": "7.36 CGPA", "tier": "tier_3"}], "skills": [{"name": "Project Management", "proficiency": "intermediate", "endorsements": 15, "duration_months": 35}, {"name": "HTML", "proficiency": "intermediate", "endorsements": 8, "duration_months": 30}, {"name": "Data Pipelines", "proficiency": "beginner", "endorsements": 13, "duration_months": 8}, {"name": "CSS", "proficiency": "beginner", "endorsements": 5, "duration_months": 8}, {"name": "Image Classification", "proficiency": "advanced", "endorsements": 37, "duration_months": 19}, {"name": "Marketing", "proficiency": "intermediate", "endorsements": 3, "duration_months": 8}, {"name": "SQL", "proficiency": "intermediate", "endorsements": 14, "duration_months": 16}, {"name": "Databricks", "proficiency": "beginner", "endorsements": 3, "duration_months": 7}], "certifications": [], "languages": [{"language": "English", "proficiency": "professional"}, {"language": "Hindi", "proficiency": "professional"}], "redrob_signals": {"profile_completeness_score": 80.5, "signup_date": "2023-02-15", "last_active_date": "2025-12-21", "open_to_work_flag": false, "profile_views_received_30d": 46, "applications_submitted_30d": 10, "recruiter_response_rate": 0.47, "avg_response_time_hours": 168.3, "skill_assessment_scores": {"Image Classification": 55.8}, "connection_count": 210, "endorsements_received": 36, "notice_period_days": 90, "expected_salary_range_inr_lpa": {"min": 7.7, "max": 8.0}, "preferred_work_mode": "hybrid", "willing_to_relocate": false, "github_activity_score": -1, "search_appearance_30d": 19, "saved_by_recruiters_30d": 4, "interview_completion_rate": 0.37, "offer_acceptance_rate": 0.36, "verified_email": true, "verified_phone": false, "linkedin_connected": false}}
{"candidate_id": "CAND_0010294", "profile": {"anonymized_name": "Reyansh Nair", "headline": ".NET Developer | Backend systems & APIs", "summary": "Software engineer with 8.0 years of experience across web, backend, and cloud systems. Strong fundamentals in software development and system design. My background is full-stack, but my comfort zone is the backend and the database. I've been keeping up with AI/ML at a self-learner level \u2014 taken some online courses, played with the OpenAI and Anthropic APIs, built a small RAG side project \u2014 but I haven't done it in a professional capacity yet. Open to roles where I can either deepen my software engineering work or, if the team is open to it, start contributing to ML-adjacent systems.", "location": "Bangalore, Karnataka", "country": "India", "years_of_experience": 8.0, "current_title": ".NET Developer", "current_company": "Mphasis", "current_company_size": "10001+", "current_industry": "IT Services"}, "career_history": [{"company": "Mphasis", "title": ".NET Developer", "start_date": "2024-11-03", "end_date": null, "duration_months": 144, "is_current": true, "industry": "IT Services", "company_size": "10001+", "description": "Test automation and QA engineering for a fintech product. Built and maintained the end-to-end test suite using Selenium and pytest, plus the load-testing setup using Locust. Worked closely with developers on testability patterns and with product on acceptance criteria. Recent work has been on shifting test responsibility into the dev team \u2014 moving from QA-as-gate to QA-as-coach. Career has been entirely in QA/test engineering."}, {"company": "Cognizant", "title": "Cloud Engineer", "start_date": "2021-04-23", "end_date": "2024-11-03", "duration_months": 43, "is_current": false, "industry": "IT Services", "company_size": "10001+", "description": "Full-stack web application development at a SaaS company. Built React-based admin interfaces and the Node.js REST API backing them. Worked across the stack: frontend components, REST endpoint design, PostgreSQL schema, deployment via Docker/Kubernetes. Comfortable in most parts of a typical web stack though my comfort zone is the backend and database side. Recent learning has been on the testing and CI/CD discipline."}, {"company": "Initech", "title": "Software Engineer", "start_date": "2018-07-24", "end_date": "2021-04-09", "duration_months": 33, "is_current": false, "industry": "Software", "company_size": "51-200", "description": "Full-stack web application development at a SaaS company. Built React-based admin interfaces and the Node.js REST API backing them. Worked across the stack: frontend components, REST endpoint design, PostgreSQL schema, deployment via Docker/Kubernetes. Comfortable in most parts of a typical web stack though my comfort zone is the backend and database side. Recent learning has been on the testing and CI/CD discipline."}], "education": [{"institution": "VIT Vellore", "degree": "B.Sc", "field_of_study": "Data Science", "start_year": 2010, "end_year": 2015, "grade": "84%", "tier": "tier_2"}, {"institution": "RV College of Engineering", "degree": "B.Sc", "field_of_study": "Data Science", "start_year": 2016, "end_year": 2019, "grade": "7.97 CGPA", "tier": "tier_2"}], "skills": [{"name": "Agile", "proficiency": "beginner", "endorsements": 14, "duration_months": 9}, {"name": "Computer Vision", "proficiency": "intermediate", "endorsements": 6, "duration_months": 22}, {"name": "Redis", "proficiency": "beginner", "endorsements": 4, "duration_months": 4}, {"name": "MongoDB", "proficiency": "beginner", "endorsements": 0, "duration_months": 16}, {"name": "Statistical Modeling", "proficiency": "intermediate", "endorsements": 10, "duration_months": 18}, {"name": "Microservices", "proficiency": "beginner", "endorsements": 0, "duration_months": 5}, {"name": "Photoshop", "proficiency": "beginner", "endorsements": 1, "duration_months": 11}, {"name": "Weaviate", "proficiency": "intermediate", "endorsements": 12, "duration_months": 8}, {"name": "Figma", "proficiency": "beginner", "endorsements": 5, "duration_months": 3}, {"name": "Go", "proficiency": "beginner", "endorsements": 6, "duration_months": 4}, {"name": "Docker", "proficiency": "beginner", "endorsements": 14, "duration_months": 8}, {"name": "SAP", "proficiency": "intermediate", "endorsements": 11, "duration_months": 28}, {"name": "MLflow", "proficiency": "advanced", "endorsements": 20, "duration_months": 45}, {"name": "Excel", "proficiency": "beginner", "endorsements": 15, "duration_months": 17}], "certifications": [{"name": "Scrum Master Certified", "issuer": "Scrum Alliance", "year": 2019}, {"name": "AWS Certified Cloud Practitioner", "issuer": "AWS", "year": 2018}], "languages": [{"language": "English", "proficiency": "professional"}, {"language": "Hindi", "proficiency": "conversational"}], "redrob_signals": {"profile_completeness_score": 56.1, "signup_date": "2026-02-19", "last_active_date": "2026-05-02", "open_to_work_flag": false, "profile_views_received_30d": 30, "applications_submitted_30d": 2, "recruiter_response_rate": 0.14, "avg_response_time_hours": 199.9, "skill_assessment_scores": {}, "connection_count": 330, "endorsements_received": 1, "notice_period_days": 90, "expected_salary_range_inr_lpa": {"min": 10.6, "max": 12.3}, "preferred_work_mode": "remote", "willing_to_relocate": false, "github_activity_score": -1, "search_appearance_30d": 51, "saved_by_recruiters_30d": 14, "interview_completion_rate": 0.82, "offer_acceptance_rate": -1, "verified_email": true, "verified_phone": true, "linkedin_connected": true}}
{"candidate_id": "CAND_0023427", "profile": {"anonymized_name": "Ela Naidu", "headline": "Full Stack Developer | Full-stack development", "summary": "Software engineer with 2.7 years of experience across web, backend, and cloud systems. Strong fundamentals in software development and system design. Most of my work is conventional backend engineering \u2014 APIs, databases, queues, deployments. I've been keeping up with AI/ML at a self-learner level \u2014 taken some online courses, played with the OpenAI and Anthropic APIs, built a small RAG side project \u2014 but I haven't done it in a professional capacity yet. Open to roles where I can either deepen my software engineering work or, if the team is open to it, start contributing to ML-adjacent systems.", "location": "Vizag, Andhra Pradesh", "country": "India", "years_of_experience": 2.7, "current_title": "Full Stack Developer", "current_company": "Pied Piper", "current_company_size": "11-50", "current_industry": "Software"}, "career_history": [{"company": "Pied Piper", "title": "Full Stack Developer", "start_date": "2024-09-04", "end_date": null, "duration_months": 21, "is_current": true, "industry": "Software", "company_size": "11-50", "description": "Android mobile development using Java and (more recently) Kotlin at a consumer-app company. Built and maintained multiple production features including the main shopping flow, push notification system, and the offline-first sync layer. Comfortable with the Android framework, Jetpack components, and the typical patterns (MVVM, Hilt, Coroutines). My career has been entirely on mobile so far; interested in expanding into broader backend or platform engineering."}, {"company": "Mphasis", "title": "Software Engineer", "start_date": "2023-11-09", "end_date": "2024-09-04", "duration_months": 10, "is_current": false, "industry": "IT Services", "company_size": "10001+", "description": "Java backend development at a large enterprise \u2014 Spring Boot microservices, Kafka for inter-service messaging, Postgres + Redis for storage. Worked on the customer onboarding flow which involved orchestrating multiple downstream services. Solid on the Spring ecosystem, transaction handling, and the operational side of Java services. Looking to either go deeper on distributed systems or expand into modern application stacks."}], "education": [{"institution": "Generic State University", "degree": "M.Sc", "field_of_study": "Computer Engineering", "start_year": 2007, "end_year": 2011, "grade": "7.81 CGPA", "tier": "tier_4"}], "skills": [{"name": "Rust", "proficiency": "beginner", "endorsements": 13, "duration_months": 18}, {"name": "React", "proficiency": "beginner", "endorsements": 6, "duration_months": 12}, {"name": "Illustrator", "proficiency": "beginner", "endorsements": 5, "duration_months": 16}, {"name": "Forecasting", "proficiency": "intermediate", "endorsements": 8, "duration_months": 21}, {"name": "Agile", "proficiency": "intermediate", "endorsements": 14, "duration_months": 34}, {"name": "Data Science", "proficiency": "intermediate", "endorsements": 2, "duration_months": 12}, {"name": "Figma", "proficiency": "beginner", "endorsements": 15, "duration_months": 18}, {"name": "Hadoop", "proficiency": "beginner", "endorsements": 12, "duration_months": 13}, {"name": "Machine Learning", "proficiency": "intermediate", "endorsements": 1, "duration_months": 14}], "certifications": [], "languages": [{"language": "English", "proficiency": "professional"}, {"language": "Hindi", "proficiency": "conversational"}], "redrob_signals": {"profile_completeness_score": 43.9, "signup_date": "2023-10-03", "last_active_date": "2025-12-23", "open_to_work_flag": true, "profile_views_received_30d": 84, "applications_submitted_30d": 1, "recruiter_response_rate": 0.73, "avg_response_time_hours": 155.9, "skill_assessment_scores": {}, "connection_count": 414, "endorsements_received": 27, "notice_period_days": 90, "expected_salary_range_inr_lpa": {"min": 12.6, "max": 29.0}, "preferred_work_mode": "hybrid", "willing_to_relocate": false, "github_activity_score": -1, "search_appearance_30d": 79, "saved_by_recruiters_30d": 8, "interview_completion_rate": 0.64, "offer_acceptance_rate": -1, "verified_email": true, "verified_phone": false, "linkedin_connected": true}}
{"candidate_id": "CAND_0083354", "profile": {"anonymized_name": "Aarohi Chatterjee", "headline": "DevOps Engineer | Cloud & DevOps", "summary": "Software engineer with 9.5 years of experience across web, backend, and cloud systems. Strong fundamentals in software development and system design. I've spent most of my career on web and API development \u2014 Python/Django and Node.js mostly. I've been keeping up with AI/ML at a self-learner level \u2014 taken some online courses, played with the OpenAI and Anthropic APIs, built a small RAG side project \u2014 but I haven't done it in a professional capacity yet. 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Looking to either go deeper on distributed systems or expand into modern application stacks."}, {"company": "Stark Industries", "title": ".NET Developer", "start_date": "2016-11-15", "end_date": "2017-06-13", "duration_months": 7, "is_current": false, "industry": "Manufacturing", "company_size": "1001-5000", "description": "Frontend engineering at a media company. React, TypeScript, and the typical surrounding tooling (Webpack, Jest, Cypress). 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{"candidate_id": "CAND_0095090", "profile": {"anonymized_name": "Aisha Khanna", "headline": "Cloud Engineer | Backend systems & APIs", "summary": "Software engineer with 5.6 years of experience across web, backend, and cloud systems. Strong fundamentals in software development and system design. I've worked across web frontends, REST APIs, and cloud deployments; comfortable in most parts of a typical SaaS stack. I've been keeping up with AI/ML at a self-learner level \u2014 taken some online courses, played with the OpenAI and Anthropic APIs, built a small RAG side project \u2014 but I haven't done it in a professional capacity yet. Open to roles where I can either deepen my software engineering work or, if the team is open to it, start contributing to ML-adjacent systems.", "location": "Mumbai, Maharashtra", "country": "India", "years_of_experience": 5.6, "current_title": "Cloud Engineer", "current_company": "Razorpay", "current_company_size": "1001-5000", "current_industry": "Fintech"}, "career_history": [{"company": "Razorpay", "title": "Cloud Engineer", "start_date": "2023-09-10", "end_date": null, "duration_months": 33, "is_current": true, "industry": "Fintech", "company_size": "1001-5000", "description": "Cloud infrastructure and DevOps work at an enterprise SaaS company. Owned the AWS account architecture (VPC, IAM, networking), the Terraform modules for our service deployments, and the Kubernetes cluster operations. Designed the CI/CD pipelines (GitLab CI + ArgoCD) and the monitoring stack (Prometheus, Grafana, Loki). Strong on the infra and ops side; haven't done much application development."}, {"company": "Swiggy", "title": "Mobile Developer", "start_date": "2020-12-24", "end_date": "2023-09-10", "duration_months": 33, "is_current": false, "industry": "Food Delivery", "company_size": "5001-10000", "description": "Frontend engineering at a media company. React, TypeScript, and the typical surrounding tooling (Webpack, Jest, Cypress). Built the company's design system from scratch and led the migration from a legacy AngularJS app. Strong on the frontend craft \u2014 accessibility, performance, animations \u2014 but limited backend exposure."}], "education": [{"institution": "SRM Chennai", "degree": "M.Tech", "field_of_study": "Commerce", "start_year": 2004, "end_year": 2007, "grade": "7.91 CGPA", "tier": "tier_3"}], "skills": [{"name": "Webpack", "proficiency": "beginner", "endorsements": 6, "duration_months": 6}, {"name": "TypeScript", "proficiency": "intermediate", "endorsements": 7, "duration_months": 36}, {"name": "Accounting", "proficiency": "intermediate", "endorsements": 13, "duration_months": 27}, {"name": "Excel", "proficiency": "beginner", "endorsements": 4, "duration_months": 7}, {"name": "Spark", "proficiency": "intermediate", "endorsements": 0, "duration_months": 19}, {"name": "Apache Flink", "proficiency": "beginner", "endorsements": 6, "duration_months": 14}, {"name": "YOLO", "proficiency": "advanced", "endorsements": 18, "duration_months": 26}, {"name": "Agile", "proficiency": "beginner", "endorsements": 0, "duration_months": 3}], "certifications": [], "languages": [{"language": "English", "proficiency": "professional"}, {"language": "Hindi", "proficiency": "native"}], "redrob_signals": {"profile_completeness_score": 54.9, "signup_date": "2026-04-08", "last_active_date": "2025-12-24", "open_to_work_flag": false, "profile_views_received_30d": 75, "applications_submitted_30d": 11, "recruiter_response_rate": 0.75, "avg_response_time_hours": 44.4, "skill_assessment_scores": {"YOLO": 44.8}, "connection_count": 390, "endorsements_received": 73, "notice_period_days": 150, "expected_salary_range_inr_lpa": {"min": 20.7, "max": 22.0}, "preferred_work_mode": "hybrid", "willing_to_relocate": false, "github_activity_score": -1, "search_appearance_30d": 261, "saved_by_recruiters_30d": 6, "interview_completion_rate": 0.68, "offer_acceptance_rate": 0.48, "verified_email": true, "verified_phone": true, "linkedin_connected": false}}