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.

Search Engineer

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 — the modeling part was almost the easy bit. Final model improved revenue-per-search by 12%.

Machine Learning Engineer

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) — that work was as important as the modeling itself.

Machine Learning Engineer

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.

Machine Learning Engineer


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 — 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.

Search Engineer

Senior Data Scientist

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.


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 — looking for senior IC or tech-lead roles where I can own the intelligence layer end-to-end.

Senior Machine Learning Engineer

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.

Senior Machine Learning Engineer

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 — 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.


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.

AI Engineer

Machine Learning Engineer

LangChain for LLM Application Development


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.

AI Engineer

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.

NLP Engineer


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 — usually it's both. Currently exploring my next move — looking for senior IC or tech-lead roles where I can own the intelligence layer end-to-end.

Senior Machine Learning Engineer

Staff Machine Learning Engineer

Senior Machine Learning Engineer

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.


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.

AI Engineer

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%.

NLP Engineer

Applied ML Engineer

AI Engineer


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 — 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.

NLP Engineer

NLP Engineer


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 — looking for senior IC or tech-lead roles where I can own the intelligence layer end-to-end.

Senior Machine Learning Engineer

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.

Senior AI Engineer

Senior Applied Scientist


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 — 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.

AI Engineer

AI Engineer


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 — 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.

Machine Learning Engineer

Applied ML Engineer

Senior Data Scientist


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.

NLP Engineer

NLP Engineer

AWS Certified Machine Learning Specialty


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 — 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.

Applied ML Engineer

AI Engineer

Recommendation Systems Engineer


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 — 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.

AI Engineer

Machine Learning Engineer

Search Engineer

Search Engineer


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 — get serious about LLMs and retrieval beyond the surface level.

Junior ML Engineer

Built recommendation-style features at a mid-stage startup — 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.

Senior Software Engineer (ML)

Built computer vision models for our product's image moderation feature using PyTorch — 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.


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 — 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 — get serious about LLMs and retrieval beyond the surface level.

ML Engineer

Data Scientist


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.

Machine Learning Engineer

Machine Learning Engineer


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.

Applied ML Engineer

Search Engineer


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 — closer to retrieval, LLMs, and modern ranking systems.

ML Engineer

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 — I was the production-side engineer.

Data Scientist


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 — closer to retrieval, LLMs, and modern ranking systems.

ML Engineer

Computer Vision Engineer

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 — 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.


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.

Machine Learning Engineer

AI Engineer


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 — 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.

NLP Engineer

NLP Engineer

Machine Learning Engineer

Search Engineer


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 — 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.

NLP Engineer

Applied ML Engineer

AI Engineer


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 — 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.

Applied ML Engineer

Machine Learning Engineer

Recommendation Systems Engineer


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.

Machine Learning Engineer

NLP Engineer


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.

Junior ML Engineer

Data Scientist

ML Engineer


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 — sentence-transformers, FAISS, the works. Along the way I've gotten comfortable with the operational side — 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.

AI Engineer

AI Engineer


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 — 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.

Search Engineer


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 — 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.

Machine Learning Engineer

AI Engineer


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 — 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.

Search Engineer

Search Engineer


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.

AI Engineer

Applied ML Engineer


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 — 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.

AI Engineer

Machine Learning Engineer

Machine Learning Engineer


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. 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.

Applied ML Engineer

Search Engineer

NLP Engineer


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 — 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.

AI Engineer

Recommendation Systems Engineer


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. 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.

Applied ML Engineer

Recommendation Systems Engineer

AI Engineer


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 — 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.

NLP Engineer

Machine Learning Engineer


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 — 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.

Applied ML Engineer

Applied ML Engineer


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 — 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.

Search Engineer

Machine Learning Engineer


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. 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.

Machine Learning Engineer

Machine Learning Engineer

NLP Engineer


AI Engineer

Senior Data Scientist

Search Engineer


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 — 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 — get serious about LLMs and retrieval beyond the surface level.

Data Scientist

Computer Vision Engineer

Worked on customer-facing predictive modeling for an e-commerce platform — 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.

ML Engineer


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 — closer to retrieval, LLMs, and modern ranking systems.

Data Scientist

AI Specialist

Senior Software Engineer (ML)


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 — 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 — closer to retrieval, LLMs, and modern ranking systems.

ML Engineer

Built NLP pipelines for sentiment analysis and document classification — 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.

AI Research Engineer

Junior ML Engineer


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 — 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.

Applied ML Engineer

Machine Learning Engineer

NLP Engineer


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.

Applied ML Engineer

Recommendation Systems Engineer


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.

NLP Engineer


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 — 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.

Search Engineer

Applied ML Engineer


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 — 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.

Applied ML Engineer

AI Engineer

Recommendation Systems Engineer

Search Engineer


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 — 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.

Machine Learning Engineer

Search Engineer

Senior Data Scientist


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 — 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 — closer to retrieval, LLMs, and modern ranking systems.

Data Scientist

Computer Vision Engineer


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 — sentence-transformers, FAISS, the works. Along the way I've gotten comfortable with the operational side — 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.

Applied ML Engineer

AI Engineer


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.

Applied ML Engineer

Search Engineer

Search Engineer


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.

Machine Learning Engineer

Machine Learning Engineer


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.

Search Engineer

Machine Learning Engineer

AI Engineer


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 — 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.

AI Engineer

Senior Data Scientist

AI Engineer

Machine Learning Engineer


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 — sentence-transformers, FAISS, the works. I care a lot about evaluation rigor — 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.

Applied ML Engineer

AI Engineer

NLP Engineer


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. 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.

Search Engineer

Applied ML Engineer


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 — 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.

ML Engineer

AI Specialist


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 — closer to retrieval, LLMs, and modern ranking systems.

Junior ML Engineer

ML Engineer

Data Scientist


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.

Search Engineer

Senior Data Scientist

Applied ML Engineer


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.

AI Engineer


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.

Data Scientist

AI Research Engineer

ML Engineer


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.

Search Engineer

NLP Engineer

Search Engineer


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 — 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.

Machine Learning Engineer

Senior Data Scientist


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 — 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 — closer to retrieval, LLMs, and modern ranking systems.

ML Engineer

AI Specialist

AI Research Engineer


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 — get serious about LLMs and retrieval beyond the surface level.

ML Engineer

ML Engineer


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 — 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 — closer to retrieval, LLMs, and modern ranking systems.

Junior ML Engineer

AI Research Engineer


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 — 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.

Machine Learning Engineer

Recommendation Systems Engineer


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.

Applied ML Engineer

Applied ML Engineer

NLP Engineer


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 — 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.

Search Engineer

Machine Learning Engineer

Machine Learning Engineer

NLP Engineer


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 — 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.

Project Manager

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.

Marketing Manager

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.

Sales Executive

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.

Customer Support

Project Manager

Customer Support

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.


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.

Recommendation Systems Engineer

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) — that work was as important as the modeling itself.

Search Engineer

NLP Engineer

Applied ML Engineer

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 — the modeling part was almost the easy bit. Final model improved revenue-per-search by 12%.


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 — 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.

Operations Manager

Marketing leadership role at a B2B SaaS company. Owned the demand-generation function — 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.


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 — first dashboards, then ETL, now some basic ML. My toolkit is solid on the data engineering side — Python, SQL, Spark, Airflow, warehouse design — 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.

Data Analyst

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.

Senior Data Engineer

Backend Engineer

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.


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 — 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.

Graphic Designer

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.

Graphic Designer

Customer Support


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 — 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.

Mechanical Engineer

Accountant


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.

AI Specialist


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 — 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.

Graphic Designer

Accountant

Sales Executive


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 — 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.

Customer Support

Project Manager


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 — 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.

Graphic Designer

Business Analyst

Sales Executive

Content writing and SEO strategy for a tech-focused publication. Wrote longform articles on developer tools, cloud platforms, and AI/ML topics — 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.

HR Manager

Graphic Designer

Senior accounting role at a mid-sized company — 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.


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 — closer to retrieval, LLMs, and modern ranking systems.

AI Specialist

Built computer vision models for our product's image moderation feature using PyTorch — 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.

ML Engineer

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 — I was the production-side engineer.


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 — 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.

Marketing Manager

Business Analyst

Marketing Manager

Content Writer

Mechanical Engineer


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 — 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.

Operations Manager

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.

Customer Support

Operations Manager


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 — first dashboards, then ETL, now some basic ML. My toolkit is solid on the data engineering side — Python, SQL, Spark, Airflow, warehouse design — 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.

Senior Data Engineer

Designed and maintained the analytical data warehouse on Snowflake supporting the BI team's ~50 dashboards. Wrote complex SQL — 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.

Backend Engineer

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.

Data Engineer


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 — 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.

Operations Manager

Sales Executive

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.


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 — 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.

Customer Support

HR Manager

Business Analyst

Business Analyst

Business Analyst


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 — 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.

Marketing Manager

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.

Business Analyst

Project Manager

Operations Manager

Operations Manager


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 — taken some online courses, played with the OpenAI and Anthropic APIs, built a small RAG side project — 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.

Java Developer

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 — moving from QA-as-gate to QA-as-coach. Career has been entirely in QA/test engineering.


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 — 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.

Content Writer

Customer Support

Mechanical Engineer

HR Manager


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 — 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.

HR Manager

Mechanical Engineer

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.

Accountant

Marketing Manager


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 — 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.

Civil Engineer

Operations Manager

Graphic Designer

Customer Support


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 — 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.

Business Analyst

Graphic Designer


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 — 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.

Machine Learning Engineer

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.

Senior Data Scientist

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%.

Machine Learning Engineer

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) — that work was as important as the modeling itself.


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 — taken some online courses, played with the OpenAI and Anthropic APIs, built a small RAG side project — 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.

Mobile Developer

DevOps Engineer

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 — accessibility, performance, animations — but limited backend exposure.

.NET Developer

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.


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 — taken some online courses, played with the OpenAI and Anthropic APIs, built a small RAG side project — 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.

Frontend Engineer

Full Stack Developer

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.

DevOps Engineer

Full Stack Developer


Graphic Designer | Helping teams scale

Summary: Professional with 10.3+ years of experience. My professional background is in marketing manager — 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 — 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.

Graphic Designer

Customer Support

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.

Customer Support

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.

Project Manager


.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 — taken some online courses, played with the OpenAI and Anthropic APIs, built a small RAG side project — 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.

.NET Developer

Cloud Engineer

Software Engineer


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 — APIs, databases, queues, deployments. I've been keeping up with AI/ML at a self-learner level — taken some online courses, played with the OpenAI and Anthropic APIs, built a small RAG side project — 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.

Full Stack Developer

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.

Software Engineer

Java backend development at a large enterprise — 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.


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 — Python/Django and Node.js mostly. I've been keeping up with AI/ML at a self-learner level — taken some online courses, played with the OpenAI and Anthropic APIs, built a small RAG side project — 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.

DevOps Engineer

.NET Developer

Frontend Engineer

Mobile Developer

.NET Developer


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 — taken some online courses, played with the OpenAI and Anthropic APIs, built a small RAG side project — 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.

Cloud Engineer

Mobile Developer