# Top-100 Candidate Discovery -- Executive Triage Dashboard Re-derived from the real `EligibilityEngine` / `ScoringEngine` / `features.skills` objects via `scripts/profile_submission_analytics.py` -- no parallel scoring math. ## Executive KPI Summary - **Full pool size:** 100000 - **Top-10 cohort average YOE:** 7.74 years - **Active honeypots caught in top 100:** 0 / 100 (verified via `IntegrityEngine`) - **Hard-blocked candidates leaking into top 100:** 0 / 100 (should always be 0 -- a nonzero count means the eligible pool fell below 100 and the ranker padded with floored rows) - **Self-learner / dabbler pattern flagged:** 43 / 100 (in_career == 0 with trust >= 0.5 on a top-3 contributing group) - **Ghost-profile penalty (behavioral_multiplier < 0.85):** 5 / 100 ### Hard-Gate Fired Rates (full pool, from latest `run_report.json`) | Gate | Fired | % of Pool | | :--- | ---: | ---: | | `primary_cv_speech_robotics_no_nlp` | 87,767 | 87.77% | | `notice_over_30` | 54,030 | 54.03% | | `pure_research_no_production` | 26,977 | 26.98% | | `outside_india_no_sponsor` | 21,957 | 21.96% | | `title_chaser_sub_18m_hops` | 18,583 | 18.58% | | `consulting_firms_only_career` | 11,875 | 11.88% | | `closed_source_5y_no_validation` | 7,540 | 7.54% | | `outside_experience_band` | 4,724 | 4.72% | ## High-Density Triage Table | Rank | Score | ID | Title @ Company | Top-3 Competency (skill_match) | Logistics & Availability | Gate / Risk Alerts | | ---: | ---: | :--- | :--- | :--- | :--- | :--- | | 1 | 0.504798 | `CAND_0041610` | **Anil Subramanian**
`Recommendation Systems Engineer` @ *Zoho*
YOE 6.7 | llm=0.264 [gpt,langchain,openai]
ir=0.235 [embeddings]
mlops=0.222 [kubeflow,mlflow]
**skill_match=0.240168** | india_relocatable / sub_30_ideal
notice 30d
resp 52% (present) | [⚠️ SOFT:title_chaser_sub_18m_hops] | | 2 | 0.503434 | `CAND_0097176` | **Advik Mehta**
`ML Engineer` @ *TCS*
YOE 5.9 | mle=0.403 [pytorch,scikit]
nlp=0.222 [nlp,transformer]
rank=0.083 [ranking]
**skill_match=0.236211** | preferred_hub / sub_30_ideal
notice 30d
resp 76% (present) | [⚠️ SOFT:title_chaser_sub_18m_hops] | | 3 | 0.500489 | `CAND_0074024` | **Shreya Singh**
`AI Specialist` @ *Haptik*
YOE 3.9 | mle=0.344 [pytorch,scikit]
nlp=0.289 [nlp,transformer]
rank=0.083 [ranking]
**skill_match=0.238921** | preferred_hub / sub_30_ideal
notice 30d
resp 50% (present) | [⚠️ SOFT:title_chaser_sub_18m_hops] | | 4 | 0.498798 | `CAND_0039754` | **Mira Banerjee**
`Senior Applied Scientist` @ *Meta*
YOE 16.2 | rank=0.250 [ranker,ranking,reranking]
ir=0.250 [embeddings,faiss,retrieval]
retr=0.237 [elasticsearch,retrieval]
**skill_match=0.245579** | india_relocatable / sub_30_ideal
notice 30d
resp 81% (present) | [✅ CLEAR] | | 5 | 0.497311 | `CAND_0018499` | **Aarav Trivedi**
`Senior Machine Learning Engineer` @ *Zomato*
YOE 7.2 | ir=0.484 [embeddings,faiss,retrieval]
llm=0.276 [gpt,langchain,llm]
rank=0.250 [ranker,ranking,reranking]
**skill_match=0.336651** | preferred_hub / sub_30_ideal
notice 15d
resp 61% (present) | [✅ CLEAR] | | 6 | 0.486131 | `CAND_0079284` | **Ishaan Dutta**
`Machine Learning Engineer` @ *Google*
YOE 4.9 | recsys=0.327 [recommendation]
nlp=0.289 [nlp,transformer]
mle=0.117 [scikit]
**skill_match=0.244135** | preferred_hub / sub_30_ideal
notice 30d
resp 79% (present) | [🐥 DABBLER_PATTERN:mle] | | 7 | 0.484576 | `CAND_0037160` | **Riya Chatterjee**
`Data Scientist` @ *Haptik*
YOE 6.0 | mle=0.222 [pytorch,scikit]
rank=0.083 [ranking]
recsys=0.083 [recommendation]
**skill_match=0.129630** | preferred_hub / sub_30_ideal
notice 30d
resp 74% (present) | [✅ CLEAR] | | 8 | 0.484494 | `CAND_0036437` | **Arjun Joshi**
`Search Engineer` @ *Rephrase.ai*
YOE 4.8 | mlops=0.364 [kubeflow,mlflow]
rank=0.167 [ranking,relevance]
retr=0.091 [elasticsearch]
**skill_match=0.207020** | india_relocatable / sub_30_ideal
notice 30d
resp 87% (present) | [🐥 DABBLER_PATTERN:retr] | | 9 | 0.480946 | `CAND_0045250` | **Priya Pandey**
`Applied ML Engineer` @ *Rephrase.ai*
YOE 6.6 | mlops=0.442 [kubeflow,mlflow]
ir=0.121 [embeddings]
rank=0.083 [ranking]
**skill_match=0.215383** | preferred_hub / sub_30_ideal
notice 15d
resp 74% (present) | [🐥 DABBLER_PATTERN:ir] | | 10 | 0.477655 | `CAND_0010770` | **Ved Mittal**
`Recommendation Systems Engineer` @ *Aganitha*
YOE 15.2 | rank=0.167 [ranking,relevance]
retr=0.083 [elasticsearch]
ir=0.083 [faiss]
**skill_match=0.111111** | india_relocatable / sub_30_ideal
notice 30d
resp 73% (present) | [⚠️ SOFT:title_chaser_sub_18m_hops] | | 11 | 0.477633 | `CAND_0061257` | **Advaith Pillai**
`Staff Machine Learning Engineer` @ *LinkedIn*
YOE 8.0 | rank=0.167 [ranking,relevance]
ir=0.058 [retrieval]
retr=0.032 [indexing,retrieval]
**skill_match=0.085623** | preferred_hub / sub_30_ideal
notice 30d
resp 87% (present) | [🐥 DABBLER_PATTERN:ir,retr] | | 12 | 0.475440 | `CAND_0069638` | **Diya Gupta**
`Computer Vision Engineer` @ *Swiggy*
YOE 6.2 | nlp=0.222 [nlp,transformer]
llm=0.129 [prompt]
mle=0.085 [pytorch]
**skill_match=0.145484** | india_relocatable / sub_30_ideal
notice 30d
resp 64% (present) | [🐥 DABBLER_PATTERN:llm] | | 13 | 0.475323 | `CAND_0068351` | **Aadhya Iyer**
`Lead AI Engineer` @ *Sarvam AI*
YOE 6.4 | rank=0.349 [ranking,relevance]
nlp=0.066 [nlp]
ir=0.059 [retrieval]
**skill_match=0.157982** | preferred_hub / sub_30_ideal
notice 0d
resp 86% (present) | [🐥 DABBLER_PATTERN:nlp,ir] | | 14 | 0.473696 | `CAND_0008239` | **Advik Iyer**
`AI Engineer` @ *Apple*
YOE 4.0 | rank=0.167 [ranking,relevance]
retr=0.076 [elasticsearch]
llm=0.059 [langchain]
**skill_match=0.100712** | india_relocatable / sub_30_ideal
notice 15d
resp 73% (present) | [🐥 DABBLER_PATTERN:retr,llm] | | 15 | 0.473340 | `CAND_0027691` | **Ayaan Goyal**
`NLP Engineer` @ *Haptik*
YOE 6.5 | mlops=0.324 [kubeflow,mlflow]
ir=0.168 [embeddings,faiss]
rank=0.167 [ranking,relevance]
**skill_match=0.219531** | preferred_hub / sub_30_ideal
notice 15d
resp 68% (present) | [✅ CLEAR] | | 16 | 0.471628 | `CAND_0042506` | **Zara Pandey**
`Search Engineer` @ *Verloop.io*
YOE 4.2 | nlp=0.299 [nlp,transformer]
ir=0.194 [embeddings,faiss,retrieval]
llm=0.133 [gpt,openai]
**skill_match=0.208681** | preferred_hub / sub_30_ideal
notice 15d
resp 48% (present) | [✅ CLEAR] | | 17 | 0.470233 | `CAND_0084283` | **Riya Naidu**
`Junior ML Engineer` @ *Sarvam AI*
YOE 3.6 | retr=0.158 [elasticsearch]
rank=0.083 [ranking]
recsys=0.083 [recommendation]
**skill_match=0.108290** | preferred_hub / sub_30_ideal
notice 30d
resp 86% (present) | [🐥 DABBLER_PATTERN:retr] | | 18 | 0.470126 | `CAND_0086022` | **Dhruv Naidu**
`Senior Applied Scientist` @ *Sarvam AI*
YOE 5.3 | ir=0.437 [embeddings,faiss,retrieval]
rank=0.250 [ranker,ranking,reranking]
eval=0.222 [mrr,ndcg]
**skill_match=0.303068** | india_relocatable / sub_30_ideal
notice 0d
resp 55% (present) | [✅ CLEAR] | | 19 | 0.469813 | `CAND_0074123` | **Karan Sen**
`Data Scientist` @ *CRED*
YOE 6.9 | nlp=0.303 [nlp,transformer]
mle=0.093 [pytorch]
rank=0.083 [ranking]
**skill_match=0.159734** | india_relocatable / sub_30_ideal
notice 30d
resp 38% (present) | [✅ CLEAR] | | 20 | 0.468495 | `CAND_0091534` | **Dhruv Dutta**
`AI Engineer` @ *Flipkart*
YOE 16.6 | llm=0.321 [gpt,langchain,openai]
rank=0.167 [ranking,relevance]
ir=0.167 [embeddings,faiss]
**skill_match=0.218116** | preferred_hub / sub_30_ideal
notice 30d
resp 84% (present) | [✅ CLEAR] | | 21 | 0.468105 | `CAND_0043860` | **Pranav Sharma**
`Junior ML Engineer` @ *Aganitha*
YOE 6.1 | mle=0.203 [pytorch]
retr=0.153 [retrieval]
ir=0.153 [retrieval]
**skill_match=0.169975** | india_relocatable / sub_30_ideal
notice 30d
resp 81% (present) | [🐥 DABBLER_PATTERN:retr,ir] | | 22 | 0.467548 | `CAND_0002025` | **Ira Dalal**
`Senior AI Engineer` @ *Apple*
YOE 5.9 | nlp=0.299 [nlp,transformer]
ir=0.240 [embeddings,faiss]
recsys=0.234 [recommendation]
**skill_match=0.257573** | india_non_relocatable / sub_30_ideal
notice 30d
resp 80% (present) | [✅ CLEAR] | | 23 | 0.467277 | `CAND_0007411` | **Rahul Bansal**
`Senior Machine Learning Engineer` @ *Amazon*
YOE 8.0 | llm=0.287 [gpt,prompt]
retr=0.243 [retrieval]
ir=0.243 [retrieval]
**skill_match=0.257717** | india_relocatable / sub_30_ideal
notice 15d
resp 12% (present) | [⚠️ GHOST_PENALTY] | | 24 | 0.461571 | `CAND_0081846` | **Arjun Khanna**
`Lead AI Engineer` @ *Razorpay*
YOE 6.7 | ir=0.505 [embeddings,faiss,retrieval]
rank=0.250 [ranker,ranking,reranking]
retr=0.225 [elasticsearch,retrieval]
**skill_match=0.326946** | india_relocatable / sub_30_ideal
notice 30d
resp 73% (present) | [✅ CLEAR] | | 25 | 0.458766 | `CAND_0057134` | **Tanvi Joshi**
`Data Scientist` @ *PhonePe*
YOE 3.0 | mle=0.118 [tensorflow]
rank=0.083 [ranking]
recsys=0.083 [recommendation]
**skill_match=0.094966** | preferred_hub / sub_30_ideal
notice 30d
resp 34% (present) | [⚠️ SOFT:title_chaser_sub_18m_hops]
[⚠️ GHOST_PENALTY]
[🐥 DABBLER_PATTERN:mle] | | 26 | 0.456814 | `CAND_0068811` | **Krishna Mittal**
`Applied ML Engineer` @ *Freshworks*
YOE 8.0 | mlops=0.346 [kubeflow,mlflow]
ir=0.220 [embeddings]
rank=0.167 [ranking,relevance]
**skill_match=0.244144** | preferred_hub / sub_30_ideal
notice 30d
resp 42% (present) | [✅ CLEAR] | | 27 | 0.447424 | `CAND_0060054` | **Aisha Kapoor**
`AI Engineer` @ *Mad Street Den*
YOE 6.4 | ir=0.370 [embeddings,faiss]
recsys=0.233 [recommendation]
retr=0.199 [elasticsearch]
**skill_match=0.267080** | india_relocatable / sub_30_ideal
notice 15d
resp 86% (present) | [✅ CLEAR] | | 28 | 0.446938 | `CAND_0010685` | **Sunil Mishra**
`NLP Engineer` @ *Rephrase.ai*
YOE 6.7 | mlops=0.430 [kubeflow,mlflow]
ir=0.237 [faiss,retrieval]
retr=0.203 [elasticsearch,retrieval]
**skill_match=0.289681** | india_non_relocatable / sub_30_ideal
notice 30d
resp 83% (present) | [✅ CLEAR] | | 29 | 0.446880 | `CAND_0094759` | **Aditya Pillai**
`Lead AI Engineer` @ *Meta*
YOE 8.6 | ir=0.434 [embeddings,faiss,retrieval]
llm=0.294 [llm,prompt]
rank=0.250 [ranker,ranking,reranking]
**skill_match=0.325955** | preferred_hub / sub_30_ideal
notice 30d
resp 11% (present) | [⚠️ GHOST_PENALTY] | | 30 | 0.446617 | `CAND_0078492` | **Aadhya Vora**
`Recommendation Systems Engineer` @ *Verloop.io*
YOE 5.1 | rank=0.167 [ranking,relevance]
ir=0.076 [faiss]
mle=0.076 [scikit]
**skill_match=0.106441** | india_relocatable / sub_30_ideal
notice 30d
resp 70% (present) | [⚠️ SOFT:title_chaser_sub_18m_hops]
[🐥 DABBLER_PATTERN:ir,mle] | | 31 | 0.446195 | `CAND_0011687` | **Shreya Tiwari**
`Senior NLP Engineer` @ *Niramai*
YOE 7.8 | llm=0.210 [gpt,langchain]
ir=0.201 [embeddings,faiss,retrieval]
retr=0.083 [retrieval]
**skill_match=0.164650** | india_non_relocatable / sub_30_ideal
notice 15d
resp 89% (present) | [✅ CLEAR] | | 32 | 0.445573 | `CAND_0022274` | **Advaith Mukherjee**
`AI Research Engineer` @ *Yellow.ai*
YOE 4.5 | recsys=0.154 [recommendation]
mle=0.111 [scikit]
llm=0.057 [prompt]
**skill_match=0.107247** | india_relocatable / sub_30_ideal
notice 30d
resp 44% (present) | [🐥 DABBLER_PATTERN:recsys,llm] | | 33 | 0.445515 | `CAND_0050454` | **Saanvi Bansal**
`AI Engineer` @ *Rephrase.ai*
YOE 6.8 | ir=0.226 [faiss]
rank=0.167 [ranking,relevance]
retr=0.083 [elasticsearch]
**skill_match=0.158603** | preferred_hub / sub_30_ideal
notice 30d
resp 77% (present) | [✅ CLEAR] | | 34 | 0.444721 | `CAND_0033179` | **Yash Subramanian**
`AI Research Engineer` @ *Wipro*
YOE 6.9 | mle=0.189 [pytorch,scikit]
rank=0.083 [ranking]
recsys=0.083 [recommendation]
**skill_match=0.118397** | india_relocatable / sub_30_ideal
notice 30d
resp 84% (present) | [✅ CLEAR] | | 35 | 0.444300 | `CAND_0080051` | **Arjun Iyer**
`Data Scientist` @ *Niramai*
YOE 5.2 | rank=0.083 [ranking]
recsys=0.083 [recommendation]
nlp=0.068 [nlp]
**skill_match=0.078255** | preferred_hub / sub_30_ideal
notice 30d
resp 54% (present) | [⚠️ SOFT:title_chaser_sub_18m_hops]
[🐥 DABBLER_PATTERN:nlp] | | 36 | 0.444202 | `CAND_0027723` | **Kabir Agarwal**
`ML Engineer` @ *Wysa*
YOE 4.2 | nlp=0.129 [nlp]
rank=0.083 [ranking]
recsys=0.083 [recommendation]
**skill_match=0.098453** | india_relocatable / sub_30_ideal
notice 30d
resp 38% (present) | [🐥 DABBLER_PATTERN:nlp] | | 37 | 0.443942 | `CAND_0087630` | **Aisha Rao**
`AI Engineer` @ *Vedantu*
YOE 7.2 | nlp=0.248 [nlp,transformer]
ir=0.142 [embeddings]
retr=0.104 [elasticsearch]
**skill_match=0.164595** | preferred_hub / sub_30_ideal
notice 30d
resp 45% (present) | [🐥 DABBLER_PATTERN:retr] | | 38 | 0.442857 | `CAND_0060472` | **Pari Gupta**
`Computer Vision Engineer` @ *Paytm*
YOE 4.4 | nlp=0.130 [nlp]
mlops=0.127 [mlflow]
mle=0.111 [scikit]
**skill_match=0.122485** | india_relocatable / sub_30_ideal
notice 30d
resp 65% (present) | [⚠️ SOFT:title_chaser_sub_18m_hops]
[🐥 DABBLER_PATTERN:nlp,mlops] | | 39 | 0.441908 | `CAND_0052195` | **Sunil Goyal**
`Computer Vision Engineer` @ *Sarvam AI*
YOE 6.3 | mle=0.111 [pytorch]
rank=0.083 [ranking]
recsys=0.083 [recommendation]
**skill_match=0.092593** | india_relocatable / sub_30_ideal
notice 30d
resp 69% (present) | [✅ CLEAR] | | 40 | 0.441840 | `CAND_0007009` | **Anika Pillai**
`Recommendation Systems Engineer` @ *Wysa*
YOE 7.9 | ir=0.356 [embeddings,faiss]
llm=0.133 [gpt,openai]
retr=0.083 [elasticsearch]
**skill_match=0.190871** | preferred_hub / sub_30_ideal
notice 30d
resp 62% (present) | [✅ CLEAR] | | 41 | 0.437630 | `CAND_0054394` | **Ela Iyengar**
`Recommendation Systems Engineer` @ *PharmEasy*
YOE 4.1 | ir=0.194 [embeddings,faiss]
rank=0.167 [ranking,relevance]
retr=0.083 [elasticsearch]
**skill_match=0.147952** | preferred_hub / sub_30_ideal
notice 30d
resp 64% (present) | [⚠️ SOFT:title_chaser_sub_18m_hops] | | 42 | 0.435968 | `CAND_0043228` | **Kiara Sen**
`Applied ML Engineer` @ *Zoho*
YOE 6.8 | mlops=0.367 [kubeflow,mlflow]
rank=0.167 [ranking,relevance]
nlp=0.142 [nlp]
**skill_match=0.225269** | india_non_relocatable / sub_30_ideal
notice 30d
resp 41% (present) | [⚠️ GHOST_PENALTY]
[🐥 DABBLER_PATTERN:nlp] | | 43 | 0.435073 | `CAND_0061655` | **Mira Banerjee**
`Machine Learning Engineer` @ *Krutrim*
YOE 4.6 | nlp=0.282 [nlp,transformer]
rank=0.167 [ranking,relevance]
recsys=0.083 [recommendation]
**skill_match=0.177393** | india_non_relocatable / sub_30_ideal
notice 15d
resp 88% (present) | [⚠️ SOFT:title_chaser_sub_18m_hops] | | 44 | 0.434987 | `CAND_0033445` | **Ved Bhatia**
`ML Engineer` @ *Vedantu*
YOE 6.8 | nlp=0.222 [nlp,transformer]
mle=0.111 [pytorch]
retr=0.105 [retrieval]
**skill_match=0.146001** | india_non_relocatable / sub_30_ideal
notice 30d
resp 94% (present) | [🐥 DABBLER_PATTERN:retr] | | 45 | 0.434329 | `CAND_0093912` | **Advik Sethi**
`Senior Data Scientist` @ *Razorpay*
YOE 5.3 | retr=0.243 [elasticsearch]
rank=0.167 [ranking,relevance]
ir=0.151 [embeddings,faiss]
**skill_match=0.186822** | india_non_relocatable / sub_30_ideal
notice 30d
resp 66% (present) | [✅ CLEAR] | | 46 | 0.434125 | `CAND_0049538` | **Sanjay Bose**
`Applied ML Engineer` @ *Saarthi.ai*
YOE 5.8 | mlops=0.510 [kubeflow,mlflow]
rank=0.167 [ranking,relevance]
retr=0.076 [elasticsearch]
**skill_match=0.250916** | india_non_relocatable / sub_30_ideal
notice 30d
resp 72% (present) | [🐥 DABBLER_PATTERN:retr] | | 47 | 0.432840 | `CAND_0037944` | **Suresh Singh**
`Senior Data Scientist` @ *Vedantu*
YOE 4.9 | ir=0.241 [embeddings]
llm=0.133 [gpt,openai]
rank=0.083 [ranking]
**skill_match=0.152655** | india_non_relocatable / sub_30_ideal
notice 30d
resp 42% (present) | [✅ CLEAR] | | 48 | 0.429882 | `CAND_0062247` | **Saanvi Trivedi**
`AI Engineer` @ *Google*
YOE 7.3 | rank=0.167 [ranking,relevance]
retr=0.127 [elasticsearch,retrieval]
ir=0.127 [faiss,retrieval]
**skill_match=0.139999** | india_relocatable / sub_30_ideal
notice 30d
resp 78% (present) | [✅ CLEAR] | | 49 | 0.429054 | `CAND_0051292` | **Shreya Chatterjee**
`Applied ML Engineer` @ *Freshworks*
YOE 5.2 | ir=0.363 [embeddings,faiss]
retr=0.216 [elasticsearch]
rank=0.167 [ranking,relevance]
**skill_match=0.248589** | india_non_relocatable / sub_30_ideal
notice 30d
resp 52% (present) | [⚠️ SOFT:title_chaser_sub_18m_hops] | | 50 | 0.427753 | `CAND_0053695` | **Sanjay Sharma**
`Recommendation Systems Engineer` @ *Meesho*
YOE 5.8 | ir=0.239 [embeddings,faiss]
rank=0.167 [ranking,relevance]
retr=0.083 [elasticsearch]
**skill_match=0.162874** | india_non_relocatable / sub_30_ideal
notice 15d
resp 60% (present) | [✅ CLEAR] | | 51 | 0.427459 | `CAND_0037566` | **Ritu Nair**
`Machine Learning Engineer` @ *LinkedIn*
YOE 6.9 | llm=0.318 [gpt,langchain,openai]
ir=0.083 [embeddings]
retr=0.076 [elasticsearch]
**skill_match=0.159333** | india_relocatable / sub_30_ideal
notice 15d
resp 50% (present) | [🐥 DABBLER_PATTERN:retr] | | 52 | 0.420569 | `CAND_0025640` | **Anjali Kapoor**
`AI Research Engineer` @ *HCL*
YOE 5.6 | nlp=0.222 [nlp,transformer]
recsys=0.194 [recommendation]
rank=0.083 [ranking]
**skill_match=0.166471** | india_non_relocatable / sub_30_ideal
notice 30d
resp 87% (present) | [✅ CLEAR] | | 53 | 0.420008 | `CAND_0030031` | **Anil Joshi**
`AI Engineer` @ *Microsoft*
YOE 5.7 | nlp=0.369 [nlp,transformer]
ir=0.265 [embeddings,retrieval]
mlops=0.222 [kubeflow,mlflow]
**skill_match=0.285263** | india_non_relocatable / sub_30_ideal
notice 30d
resp 94% (present) | [✅ CLEAR] | | 54 | 0.419834 | `CAND_0065878` | **Suresh Kapoor**
`Senior Data Scientist` @ *Niramai*
YOE 7.8 | recsys=0.243 [recommendation]
rank=0.167 [ranking,relevance]
nlp=0.111 [transformer]
**skill_match=0.173591** | india_non_relocatable / sub_30_ideal
notice 15d
resp 48% (present) | [✅ CLEAR] | | 55 | 0.419385 | `CAND_0079387` | **Sneha Arora**
`AI Engineer` @ *Microsoft*
YOE 6.9 | mlops=0.357 [kubeflow,mlflow]
recsys=0.336 [recommendation]
ir=0.167 [embeddings,faiss]
**skill_match=0.286724** | india_non_relocatable / sub_30_ideal
notice 30d
resp 81% (present) | [✅ CLEAR] | | 56 | 0.418675 | `CAND_0080534` | **Manish Iyer**
`ML Engineer` @ *Genpact AI*
YOE 3.8 | retr=0.148 [elasticsearch]
rank=0.083 [ranking]
recsys=0.083 [recommendation]
**skill_match=0.104748** | india_non_relocatable / sub_30_ideal
notice 30d
resp 91% (present) | [⚠️ SOFT:title_chaser_sub_18m_hops]
[🐥 DABBLER_PATTERN:retr] | | 57 | 0.416002 | `CAND_0009024` | **Avni Sharma**
`Search Engineer` @ *Google*
YOE 5.2 | ir=0.300 [embeddings,faiss]
recsys=0.226 [recommendation]
nlp=0.111 [transformer]
**skill_match=0.212158** | india_non_relocatable / sub_30_ideal
notice 30d
resp 46% (present) | [✅ CLEAR] | | 58 | 0.414474 | `CAND_0073675` | **Anil Goyal**
`Data Analyst` @ *Ola*
YOE 6.4 | retr=0.129 [retrieval]
ir=0.129 [retrieval]
nlp=0.058 [nlp]
**skill_match=0.105463** | india_non_relocatable / sub_30_ideal
notice 30d
resp 73% (present) | [🐥 DABBLER_PATTERN:retr,ir,nlp] | | 59 | 0.414383 | `CAND_0040117` | **Aisha Sen**
`Recommendation Systems Engineer` @ *PhonePe*
YOE 6.5 | llm=0.271 [gpt,langchain,openai,prompt]
ir=0.220 [embeddings,faiss]
nlp=0.111 [transformer]
**skill_match=0.200813** | india_non_relocatable / sub_30_ideal
notice 15d
resp 66% (present) | [✅ CLEAR] | | 60 | 0.412898 | `CAND_0036863` | **Vikram Bansal**
`Senior Data Scientist` @ *upGrad*
YOE 4.3 | llm=0.443 [gpt,openai,prompt]
ir=0.222 [embeddings,faiss]
rank=0.167 [ranking,relevance]
**skill_match=0.277052** | india_relocatable / buyoutable
notice 60d
resp 46% (present) | [⚠️ SOFT:title_chaser_sub_18m_hops] | | 61 | 0.412740 | `CAND_0068964` | **Avni Malhotra**
`ML Engineer` @ *Mad Street Den*
YOE 4.8 | nlp=0.352 [nlp,transformer]
mle=0.222 [pytorch,scikit]
retr=0.032 [elasticsearch]
**skill_match=0.202032** | india_non_relocatable / sub_30_ideal
notice 30d
resp 70% (present) | [⚠️ SOFT:title_chaser_sub_18m_hops]
[🐥 DABBLER_PATTERN:retr] | | 62 | 0.408773 | `CAND_0006418` | **Rahul Mukherjee**
`Machine Learning Engineer` @ *Verloop.io*
YOE 5.7 | mlops=0.428 [kubeflow,mlflow]
ir=0.181 [embeddings]
rank=0.083 [ranking]
**skill_match=0.230848** | preferred_hub / buyoutable
notice 60d
resp 92% (present) | [🐥 DABBLER_PATTERN:ir] | | 63 | 0.408515 | `CAND_0052682` | **Ira Mukherjee**
`NLP Engineer` @ *Aganitha*
YOE 6.6 | ir=0.168 [embeddings,faiss]
llm=0.133 [gpt,openai]
nlp=0.111 [transformer]
**skill_match=0.137316** | india_non_relocatable / sub_30_ideal
notice 30d
resp 88% (present) | [✅ CLEAR] | | 64 | 0.404030 | `CAND_0075439` | **Pooja Mehta**
`Machine Learning Engineer` @ *Flipkart*
YOE 4.3 | ir=0.183 [embeddings,retrieval]
llm=0.133 [gpt,openai]
nlp=0.111 [transformer]
**skill_match=0.142354** | india_non_relocatable / sub_30_ideal
notice 30d
resp 56% (present) | [✅ CLEAR] | | 65 | 0.398984 | `CAND_0015528` | **Aisha Reddy**
`Applied ML Engineer` @ *Krutrim*
YOE 7.4 | mlops=0.222 [kubeflow,mlflow]
ir=0.195 [embeddings,faiss,retrieval]
nlp=0.111 [transformer]
**skill_match=0.176093** | india_non_relocatable / sub_30_ideal
notice 30d
resp 53% (present) | [⚠️ GHOST_PENALTY] | | 66 | 0.397726 | `CAND_0077337` | **Aarav Agarwal**
`Staff Machine Learning Engineer` @ *Paytm*
YOE 7.0 | ir=0.381 [embeddings,retrieval]
retr=0.240 [retrieval]
recsys=0.228 [recommendation]
**skill_match=0.282757** | india_relocatable / buyoutable
notice 60d
resp 95% (present) | [⚠️ SOFT:title_chaser_sub_18m_hops] | | 67 | 0.397416 | `CAND_0036184` | **Riya Chopra**
`Recommendation Systems Engineer` @ *CRED*
YOE 6.0 | ir=0.173 [embeddings,faiss]
retr=0.083 [elasticsearch]
rank=0.083 [relevance]
**skill_match=0.113327** | india_non_relocatable / sub_30_ideal
notice 30d
resp 90% (present) | [✅ CLEAR] | | 68 | 0.395675 | `CAND_0017590` | **Sunil Agarwal**
`AI Research Engineer` @ *Genpact AI*
YOE 5.1 | nlp=0.456 [nlp,transformer]
mle=0.256 [pytorch,tensorflow]
ir=0.097 [faiss]
**skill_match=0.269629** | india_relocatable / buyoutable
notice 45d
resp 56% (present) | [⚠️ SOFT:title_chaser_sub_18m_hops]
[🐥 DABBLER_PATTERN:ir] | | 69 | 0.395627 | `CAND_0020708` | **Kiara Patel**
`Search Engineer` @ *PolicyBazaar*
YOE 4.2 | retr=0.188 [elasticsearch]
nlp=0.088 [nlp]
rank=0.083 [relevance]
**skill_match=0.119810** | india_non_relocatable / sub_30_ideal
notice 30d
resp 84% (present) | [🐥 DABBLER_PATTERN:nlp] | | 70 | 0.395114 | `CAND_0058688` | **Anjali Kapoor**
`AI Engineer` @ *Vedantu*
YOE 6.7 | mlops=0.222 [kubeflow,mlflow]
ir=0.170 [embeddings,retrieval]
nlp=0.111 [transformer]
**skill_match=0.167854** | outside_india_no_sponsor / sub_30_ideal
notice 15d
resp 74% (present) | [⚠️ SOFT:outside_india_no_sponsor] | | 71 | 0.394665 | `CAND_0041669` | **Aisha Banerjee**
`Recommendation Systems Engineer` @ *CRED*
YOE 8.0 | llm=0.331 [gpt,openai,prompt]
ir=0.204 [embeddings,faiss,retrieval]
rank=0.167 [ranking,relevance]
**skill_match=0.233692** | preferred_hub / buyoutable
notice 60d
resp 77% (present) | [✅ CLEAR] | | 72 | 0.394459 | `CAND_0018549` | **Mira Verma**
`Recommendation Systems Engineer` @ *Uber*
YOE 6.8 | retr=0.183 [elasticsearch]
rank=0.167 [ranking,relevance]
nlp=0.111 [transformer]
**skill_match=0.153449** | india_relocatable / buyoutable
notice 60d
resp 73% (present) | [🐥 DABBLER_PATTERN:retr] | | 73 | 0.394358 | `CAND_0075574` | **Karan Ghosh**
`Machine Learning Engineer` @ *Haptik*
YOE 5.7 | rank=0.167 [ranking,relevance]
llm=0.133 [gpt,openai]
recsys=0.102 [recommendation]
**skill_match=0.134118** | india_relocatable / buyoutable
notice 60d
resp 58% (present) | [🐥 DABBLER_PATTERN:recsys] | | 74 | 0.393851 | `CAND_0098846` | **Shreya Saxena**
`AI Engineer` @ *upGrad*
YOE 7.6 | mlops=0.346 [kubeflow,mlflow,mlops]
rank=0.167 [ranking,relevance]
llm=0.133 [gpt,openai]
**skill_match=0.215298** | india_relocatable / buyoutable
notice 45d
resp 62% (present) | [✅ CLEAR] | | 75 | 0.393534 | `CAND_0070333` | **Karan Kumar**
`AI Research Engineer` @ *TCS*
YOE 4.7 | ir=0.174 [faiss]
rank=0.083 [ranking]
recsys=0.083 [recommendation]
**skill_match=0.113540** | india_relocatable / buyoutable
notice 60d
resp 48% (present) | [🐥 DABBLER_PATTERN:ir] | | 76 | 0.392451 | `CAND_0082086` | **Aryan Krishnan**
`Senior Software Engineer (ML)` @ *Razorpay*
YOE 6.0 | mle=0.256 [pytorch]
nlp=0.222 [nlp,transformer]
ir=0.133 [faiss]
**skill_match=0.203929** | preferred_hub / buyoutable
notice 45d
resp 85% (present) | [🐥 DABBLER_PATTERN:ir] | | 77 | 0.391200 | `CAND_0006538` | **Pooja Malhotra**
`AI Specialist` @ *Dream11*
YOE 5.7 | nlp=0.222 [nlp,transformer]
mle=0.196 [pytorch,scikit,tensorflow]
recsys=0.095 [recommendation]
**skill_match=0.171148** | india_relocatable / buyoutable
notice 60d
resp 86% (present) | [✅ CLEAR] | | 78 | 0.388814 | `CAND_0048534` | **Mira Dutta**
`Computer Vision Engineer` @ *Genpact AI*
YOE 3.4 | nlp=0.222 [nlp,transformer]
mle=0.222 [pytorch,scikit]
ir=0.029 [embeddings,retrieval]
**skill_match=0.157704** | india_relocatable / buyoutable
notice 60d
resp 80% (present) | [🐥 DABBLER_PATTERN:ir] | | 79 | 0.388281 | `CAND_0006209` | **Dhruv Dalal**
`AI Specialist` @ *Aganitha*
YOE 3.9 | nlp=0.222 [nlp,transformer]
mle=0.184 [pytorch]
retr=0.126 [retrieval]
**skill_match=0.177485** | preferred_hub / buyoutable
notice 45d
resp 66% (present) | [🐥 DABBLER_PATTERN:retr] | | 80 | 0.386574 | `CAND_0084681` | **Ritu Agarwal**
`AI Specialist` @ *Zomato*
YOE 3.5 | retr=0.167 [retrieval]
ir=0.167 [retrieval]
rank=0.083 [ranking]
**skill_match=0.138951** | india_relocatable / buyoutable
notice 60d
resp 67% (present) | [🐥 DABBLER_PATTERN:retr,ir] | | 81 | 0.385921 | `CAND_0067866` | **Kiara Sethi**
`Senior Software Engineer (ML)` @ *Tech Mahindra*
YOE 6.4 | nlp=0.222 [nlp,transformer]
mle=0.111 [pytorch]
rank=0.083 [ranking]
**skill_match=0.138889** | preferred_hub / buyoutable
notice 45d
resp 79% (present) | [✅ CLEAR] | | 82 | 0.385901 | `CAND_0078262` | **Amit Joshi**
`ML Engineer` @ *Yellow.ai*
YOE 4.9 | recsys=0.095 [recommendation]
rank=0.083 [ranking]
ir=0.049 [embeddings,faiss]
**skill_match=0.076061** | india_relocatable / buyoutable
notice 60d
resp 39% (present) | [⚠️ SOFT:title_chaser_sub_18m_hops]
[🐥 DABBLER_PATTERN:ir] | | 83 | 0.385780 | `CAND_0054703` | **Sai Shetty**
`Computer Vision Engineer` @ *Razorpay*
YOE 6.8 | nlp=0.481 [nlp,transformer]
mle=0.222 [pytorch,scikit]
rank=0.083 [ranking]
**skill_match=0.262211** | india_relocatable / buyoutable
notice 60d
resp 41% (present) | [⚠️ SOFT:title_chaser_sub_18m_hops] | | 84 | 0.385091 | `CAND_0046525` | **Tanvi Mukherjee**
`Senior Machine Learning Engineer` @ *Genpact AI*
YOE 6.1 | ir=0.546 [embeddings,faiss,retrieval]
llm=0.263 [langchain,llm]
rank=0.250 [ranker,ranking,reranking]
**skill_match=0.352977** | preferred_hub / buyoutable
notice 60d
resp 88% (present) | [✅ CLEAR] | | 85 | 0.383732 | `CAND_0060257` | **Sai Chowdary**
`AI Specialist` @ *Paytm*
YOE 5.8 | recsys=0.282 [recommendation]
mle=0.111 [scikit]
rank=0.083 [ranking]
**skill_match=0.158877** | preferred_hub / buyoutable
notice 60d
resp 57% (present) | [✅ CLEAR] | | 86 | 0.382973 | `CAND_0012837` | **Myra Chowdary**
`Junior ML Engineer` @ *Sarvam AI*
YOE 6.4 | mlops=0.151 [mlops]
mle=0.111 [pytorch]
rank=0.083 [ranking]
**skill_match=0.115133** | india_relocatable / buyoutable
notice 60d
resp 81% (present) | [🐥 DABBLER_PATTERN:mlops] | | 87 | 0.380854 | `CAND_0034177` | **Shaurya Sen**
`Senior Software Engineer (ML)` @ *Mad Street Den*
YOE 3.6 | rank=0.083 [ranking]
recsys=0.083 [recommendation]
nlp=0.062 [nlp]
**skill_match=0.076077** | india_relocatable / buyoutable
notice 60d
resp 82% (present) | [🐥 DABBLER_PATTERN:nlp] | | 88 | 0.380681 | `CAND_0011327` | **Pranav Krishnan**
`AI Research Engineer` @ *Krutrim*
YOE 6.3 | nlp=0.222 [nlp,transformer]
mle=0.111 [pytorch]
rank=0.083 [ranking]
**skill_match=0.138889** | preferred_hub / buyoutable
notice 60d
resp 79% (present) | [⚠️ SOFT:title_chaser_sub_18m_hops] | | 89 | 0.380019 | `CAND_0042029` | **Dhruv Joshi**
`Senior Data Scientist` @ *Flipkart*
YOE 6.5 | ir=0.216 [embeddings]
nlp=0.171 [nlp]
llm=0.133 [gpt,openai]
**skill_match=0.173614** | preferred_hub / buyoutable
notice 45d
resp 67% (present) | [🐥 DABBLER_PATTERN:nlp] | | 90 | 0.379223 | `CAND_0017648` | **Arnav Mishra**
`AI Specialist` @ *Freshworks*
YOE 4.0 | nlp=0.222 [nlp,transformer]
mle=0.111 [pytorch]
rank=0.083 [ranking]
**skill_match=0.138889** | preferred_hub / over_30_higher_bar
notice 120d
resp 62% (present) | [⚠️ SOFT:notice_over_30]
[⚠️ SOFT:title_chaser_sub_18m_hops] | | 91 | 0.379216 | `CAND_0038099` | **Sneha Pandey**
`Junior ML Engineer` @ *Locobuzz*
YOE 5.4 | rank=0.083 [ranking]
recsys=0.083 [recommendation]
llm=0.074 [prompt]
**skill_match=0.080357** | india_non_relocatable / over_30_higher_bar
notice 90d
resp 81% (present) | [⚠️ SOFT:notice_over_30]
[🐥 DABBLER_PATTERN:llm] | | 92 | 0.378712 | `CAND_0098952` | **Dev Sethi**
`AI Research Engineer` @ *CRED*
YOE 5.5 | recsys=0.317 [recommendation]
nlp=0.182 [nlp]
rank=0.083 [ranking]
**skill_match=0.194032** | preferred_hub / buyoutable
notice 45d
resp 66% (present) | [🐥 DABBLER_PATTERN:nlp] | | 93 | 0.378303 | `CAND_0081321` | **Avni Dalal**
`Senior Software Engineer (ML)` @ *Freshworks*
YOE 5.3 | mle=0.229 [pytorch]
nlp=0.222 [nlp,transformer]
rank=0.083 [ranking]
**skill_match=0.178238** | india_relocatable / over_30_higher_bar
notice 120d
resp 47% (present) | [⚠️ SOFT:notice_over_30] | | 94 | 0.378048 | `CAND_0024203` | **Dhruv Khanna**
`Junior ML Engineer` @ *Razorpay*
YOE 4.4 | mle=0.263 [pytorch,tensorflow]
retr=0.173 [elasticsearch]
rank=0.083 [ranking]
**skill_match=0.172979** | india_relocatable / buyoutable
notice 45d
resp 27% (present) | [⚠️ SOFT:title_chaser_sub_18m_hops]
[🐥 DABBLER_PATTERN:retr] | | 95 | 0.377870 | `CAND_0081053` | **Om Chopra**
`NLP Engineer` @ *Glance*
YOE 5.4 | mlops=0.222 [kubeflow,mlflow]
nlp=0.111 [transformer]
recsys=0.083 [recommendation]
**skill_match=0.138889** | india_relocatable / over_30_higher_bar
notice 90d
resp 83% (present) | [⚠️ SOFT:notice_over_30] | | 96 | 0.377837 | `CAND_0064904` | **Karan Trivedi**
`AI Engineer` @ *LinkedIn*
YOE 4.9 | mlops=0.450 [kubeflow,mlflow,mlops]
ir=0.321 [embeddings]
recsys=0.216 [recommendation]
**skill_match=0.328850** | preferred_hub / over_30_higher_bar
notice 90d
resp 78% (present) | [⚠️ SOFT:notice_over_30] | | 97 | 0.377229 | `CAND_0007874` | **Diya Desai**
`Junior ML Engineer` @ *Sarvam AI*
YOE 3.7 | nlp=0.158 [nlp]
mle=0.111 [pytorch]
rank=0.083 [ranking]
**skill_match=0.117580** | india_relocatable / buyoutable
notice 60d
resp 75% (present) | [🐥 DABBLER_PATTERN:nlp] | | 98 | 0.376549 | `CAND_0070589` | **Aarav Tiwari**
`ML Engineer` @ *Meesho*
YOE 3.5 | llm=0.143 [langchain]
nlp=0.129 [nlp]
rank=0.083 [ranking]
**skill_match=0.118449** | india_relocatable / buyoutable
notice 60d
resp 74% (present) | [🐥 DABBLER_PATTERN:llm,nlp] | | 99 | 0.376543 | `CAND_0044753` | **Deepak Mukherjee**
`Backend Engineer` @ *Flipkart*
YOE 5.6 | recsys=0.138 [recommendation]
retr=0.091 [retrieval]
ir=0.091 [retrieval]
**skill_match=0.106405** | india_relocatable / over_30_higher_bar
notice 120d
resp 65% (present) | [⚠️ SOFT:notice_over_30]
[⚠️ SOFT:title_chaser_sub_18m_hops]
[🐥 DABBLER_PATTERN:recsys,retr,ir] | | 100 | 0.376483 | `CAND_0050876` | **Vivaan Shah**
`Applied ML Engineer` @ *Freshworks*
YOE 6.0 | ir=0.300 [embeddings,faiss]
rank=0.167 [ranking,relevance]
nlp=0.111 [transformer]
**skill_match=0.192481** | india_non_relocatable / over_30_higher_bar
notice 90d
resp 67% (present) | [⚠️ SOFT:notice_over_30] | ## Model Generation Reasoning Manifest | Rank | ID | Score (6dp) | Reasoning | | ---: | :--- | ---: | :--- | | 1 | `CAND_0041610` | 0.504798 | *"Elite alignment -- the work at InMobi is described in the language of someone who built things themselves: a well-grounded hands-on engineering signal, not an architectural or managerial one. Weighed against that, tenure stability is the soft spot here: 50% of roles under 18 months, averaging 20 months apiece, which tempers the otherwise positive read of this profile."* | | 2 | `CAND_0097176` | 0.503434 | *"Exceptional fit -- hybrid retrieval technology visible in the skills: FAISS -- a credible alignment with the JD's hybrid-retrieval requirement. However, tenure stability is the soft spot here: 67% of roles under 18 months, averaging 23 months apiece, which tempers the otherwise positive read of this profile."* | | 3 | `CAND_0074024` | 0.500489 | *"Exceptional fit -- a credible production ML signal from Haptik: not just model training but live inference with the operational responsibility that comes with it. Worth flagging: 50% of recent roles ran under 18 months -- a retention risk the strengths above don't erase on their own."* | | 4 | `CAND_0039754` | 0.498798 | *"Exceptional fit -- hybrid retrieval experience at Apple: the description shows familiarity with the full retrieval stack (dense + sparse), not just one approach -- a marquee fit for what the JD asks."* | | 5 | `CAND_0018499` | 0.497311 | *"Exceptional fit -- Zomato shows up as a retrieval/ranking context -- exactly the core domain this role requires; a best-of-pool case for genuine domain depth rather than keyword proximity."* | | 6 | `CAND_0079284` | 0.486131 | *"Top-tier candidate -- retrieval and ranking domain depth is visible at Swiggy: the work described puts this squarely in the JD's core domain, not just adjacent to it -- a solid signal."* | | 7 | `CAND_0037160` | 0.484576 | *"Standout case -- at Haptik, the engineering context reads as production ML -- deployed systems, live traffic, real operational constraints; a solid answer to the JD's first-order requirement."* | | 8 | `CAND_0036437` | 0.484494 | *"Exceptional fit -- FAISS in the skills section is the JD's hybrid-retrieval signal in concrete form; a dependable match for this specific technical ask."* | | 9 | `CAND_0045250` | 0.480946 | *"Elite alignment -- hybrid retrieval technology visible in the skills: BM25 -- a sound alignment with the JD's hybrid-retrieval requirement."* | | 10 | `CAND_0010770` | 0.477655 | *"Elite alignment -- product-company tenure is one of this JD's core filters; Aganitha in AI/ML clears it -- a high-conviction footing on the most important background requirement. Worth flagging: job-hopping is a real concern -- average tenure runs around 22 months per role, a 50% hop rate."* | | 11 | `CAND_0061257` | 0.477633 | *"Passable fit -- the JD hires for retrieval/ranking expertise built in production; Yellow.ai is where this candidate has done it -- a respectable and specific answer to the role's core ask."* | | 12 | `CAND_0069638` | 0.475440 | *"Passable fit -- a credible production ML signal from Swiggy: not just model training but live inference with the operational responsibility that comes with it."* | | 13 | `CAND_0068351` | 0.475323 | *"Solid fit -- retrieval and ranking domain depth is visible at Sarvam AI: the work described puts this squarely in the JD's core domain, not just adjacent to it -- a competitive signal."* | | 14 | `CAND_0008239` | 0.473696 | *"Solid fit -- the JD is written for someone who has shipped to end users inside a product company; Apple (Consumer Electronics) is exactly that context -- a dependable fit on this screen."* | | 15 | `CAND_0027691` | 0.473340 | *"Workable match -- shipping ML to real users is the hardest signal to fake on a resume, and Haptik's context makes a dependable case that this candidate has actually done it."* | | 16 | `CAND_0042506` | 0.471628 | *"Passable fit -- Verloop.io (Conversational AI) is a product-company context; the career has spent a respectable portion of its time building for users, not billing clients."* | | 17 | `CAND_0084283` | 0.470233 | *"Reasonable case -- Sarvam AI is a solid product-company context; across 1 product role this person has built for end users rather than for clients."* | | 18 | `CAND_0086022` | 0.470126 | *"Passable fit -- product-company tenure is one of this JD's core filters; Sarvam AI in AI/ML clears it -- a credible footing on the most important background requirement."* | | 19 | `CAND_0074123` | 0.469813 | *"Passable fit -- consistently at product companies -- CRED (Fintech) is the kind of org this JD targets, and the tenure there is a premier match for what's being asked."* | | 20 | `CAND_0091534` | 0.468495 | *"Reasonable case -- Glance context is described with vocabulary of someone who codes and ships rather than delegates -- a credible IC signal for a role that values engineering execution over org-chart seniority."* | | 21 | `CAND_0043860` | 0.468105 | *"Solid fit -- hands-on engineering at Aganitha: the description uses builder language -- shipped, built, deployed -- not just 'oversaw' or 'designed'; a credible IC execution signal for a role that needs exactly this."* | | 22 | `CAND_0002025` | 0.467548 | *"Reasonable case -- product-company background at Apple (Consumer Electronics) -- the JD explicitly screens for this context, and a competitive fraction of this career is product-company tenure."* | | 23 | `CAND_0007411` | 0.467277 | *"Passable fit -- shipping ML to real users is the hardest signal to fake on a resume, and Yellow.ai's context makes a robust case that this candidate has actually done it. That said, a 12% recruiter response rate is the friction point here -- most cold outreach goes unanswered, which changes the sourcing approach needed."* | | 24 | `CAND_0081846` | 0.461571 | *"Solid fit -- Razorpay context is described with vocabulary of someone who codes and ships rather than delegates -- a respectable IC signal for a role that values engineering execution over org-chart seniority."* | | 25 | `CAND_0057134` | 0.458766 | *"Passable fit -- domain match on retrieval/ranking: the PhonePe experience puts this in the JD's sweet spot rather than at the margins. The one caveat: retention risk shows up clearly in the tenure data: shortest role at 12 months, average at 18, hop rate at 50% -- all worth surfacing before a final call."* | | 26 | `CAND_0068811` | 0.456814 | *"Good alignment -- the Freshworks description shows evaluation-aware engineering -- ranking metrics are referenced, differentiating this from candidates who tune systems by intuition alone."* | | 27 | `CAND_0060054` | 0.447424 | *"Workable match -- the JD hires for retrieval/ranking expertise built in production; Mad Street Den is where this candidate has done it -- a best-in-class and specific answer to the role's core ask."* | | 28 | `CAND_0010685` | 0.446938 | *"Workable match -- domain match on retrieval/ranking: the Mad Street Den experience puts this in the JD's sweet spot rather than at the margins."* | | 29 | `CAND_0094759` | 0.446880 | *"Good alignment -- production ML track record at Apple: the work described goes beyond experimentation into live systems with real-user stakes -- a competitive signal for this role's primary bar. Even so, 11% response rate to recruiter messages -- a practical sourcing obstacle that warrants a warm intro or a different reach channel rather than cold outreach."* | | 30 | `CAND_0078492` | 0.446617 | *"Reasonable case -- FAISS in the skills section is the JD's hybrid-retrieval signal in concrete form; a robust match for this specific technical ask. The one caveat: job-hopping is a real concern -- average tenure runs around 30 months per role, a 50% hop rate."* | | 31 | `CAND_0011687` | 0.446195 | *"Reasonable case -- product-company background at Niramai (HealthTech AI) -- the JD explicitly screens for this context, and a competitive fraction of this career is product-company tenure."* | | 32 | `CAND_0022274` | 0.445573 | *"Workable match -- evaluation rigour shows up explicitly: Information Retrieval is listed as a skill, and the JD specifically calls for proper offline/online evaluation -- a strong signal this isn't a vibes-based engineer."* | | 33 | `CAND_0050454` | 0.445515 | *"A fair match -- Rephrase.ai (AI/ML) is a product-company context; the career has spent a top-bracket portion of its time building for users, not billing clients."* | | 34 | `CAND_0033179` | 0.444721 | *"A fair match -- the JD's primary bar is production ML; the Wipro context in this profile clears it -- the work is framed in deployment terms, not research terms."* | | 35 | `CAND_0080051` | 0.444300 | *"Workable match -- Niramai shows up as a context where ML went to production, not just to a notebook -- the description puts this in a well-rounded tier for what the role is actually asking for. Weighed against that, the shortest stint on record ran just 14 months, and with a 50% hop rate overall, retention is a fair question to raise before extending an offer."* | | 36 | `CAND_0027723` | 0.444202 | *"A fair match -- Wysa shows up as a context where ML went to production, not just to a notebook -- the description puts this in a sound tier for what the role is actually asking for."* | | 37 | `CAND_0087630` | 0.443942 | *"Good alignment -- Vedantu context is described with vocabulary of someone who codes and ships rather than delegates -- an above-average IC signal for a role that values engineering execution over org-chart seniority."* | | 38 | `CAND_0060472` | 0.442857 | *"A fair match -- product-company background at Paytm (Fintech) -- the JD explicitly screens for this context, and a strong fraction of this career is product-company tenure. That said, the tenure curve here -- 14 months at the shortest, 26 on average -- puts the hop rate at 50% and is the main thing standing between a strong profile and an easy yes."* | | 39 | `CAND_0052195` | 0.441908 | *"Reasonable case -- shipping ML to real users is the hardest signal to fake on a resume, and Sarvam AI's context makes a dependable case that this candidate has actually done it."* | | 40 | `CAND_0007009` | 0.441840 | *"Solid fit -- Wysa is a top-shelf product-company context; across 3 product roles this person has built for end users rather than for clients."* | | 41 | `CAND_0054394` | 0.437630 | *"Good alignment -- the JD asks for hybrid retrieval; the PharmEasy context suggests an above-average hands-on exposure to both dense and sparse methods rather than expertise in one only. The one caveat: job-hopping is a real concern -- average tenure runs around 24 months per role, a 50% hop rate."* | | 42 | `CAND_0043228` | 0.435968 | *"Reasonable case -- the JD asks for hybrid retrieval; the Yellow.ai context suggests a well-rounded hands-on exposure to both dense and sparse methods rather than expertise in one only."* | | 43 | `CAND_0061655` | 0.435073 | *"Passable fit -- evaluation framework usage in evidence at Google: the role description references ranking metrics (NDCG, MRR, or similar), which is the rigour signal the JD is hiring for. Still, job-hopping is a real concern -- average tenure runs around 27 months per role, a 50% hop rate."* | | 44 | `CAND_0033445` | 0.434987 | *"Good alignment -- product-company tenure is one of this JD's core filters; Vedantu in EdTech clears it -- a top-tier footing on the most important background requirement."* | | 45 | `CAND_0093912` | 0.434329 | *"Passable fit -- consistently at product companies -- Razorpay (Fintech) is the kind of org this JD targets, and the tenure there is a high-conviction match for what's being asked."* | | 46 | `CAND_0049538` | 0.434125 | *"Workable match -- Saarthi.ai (Voice AI) is a product-company context; the career has spent a flagship portion of its time building for users, not billing clients."* | | 47 | `CAND_0037944` | 0.432840 | *"Solid fit -- Vedantu (EdTech) is a product-company context; the career has spent a respectable portion of its time building for users, not billing clients."* | | 48 | `CAND_0062247` | 0.429882 | *"Good alignment -- Google is a solid product-company context; across 2 product roles this person has built for end users rather than for clients."* | | 49 | `CAND_0051292` | 0.429054 | *"Passable fit -- Freshworks is a standout product-company context; across 3 product roles this person has built for end users rather than for clients. Weighed against that, job-hopping is a real concern -- average tenure runs around 20 months per role, a 67% hop rate."* | | 50 | `CAND_0053695` | 0.427753 | *"Passable fit -- hands-on engineering at Meesho: the description uses builder language -- shipped, built, deployed -- not just 'oversaw' or 'designed'; a respectable IC execution signal for a role that needs exactly this."* | | 51 | `CAND_0037566` | 0.427459 | *"Workable match -- the work at LinkedIn is described in the language of someone who built things themselves: a credible hands-on engineering signal, not an architectural or managerial one."* | | 52 | `CAND_0025640` | 0.420569 | *"A fair match -- production ML track record at HCL: the work described goes beyond experimentation into live systems with real-user stakes -- a competitive signal for this role's primary bar."* | | 53 | `CAND_0030031` | 0.420008 | *"Passable fit -- core domain evidence at Microsoft -- ranking and retrieval work in a production context; a well-rounded fit for this role's most specific technical requirement."* | | 54 | `CAND_0065878` | 0.419834 | *"Reasonable case -- shipping ML to real users is the hardest signal to fake on a resume, and Uber's context makes a robust case that this candidate has actually done it."* | | 55 | `CAND_0079387` | 0.419385 | *"Reasonable case -- Microsoft (Software) is a product-company context; the career has spent a top-shelf portion of its time building for users, not billing clients."* | | 56 | `CAND_0080534` | 0.418675 | *"Workable match -- the work at Genpact AI is described in the language of someone who built things themselves: a respectable hands-on engineering signal, not an architectural or managerial one. However, 22 months is the average stay here, and at a 50% hop rate this looks more like a pattern than a one-off job change."* | | 57 | `CAND_0009024` | 0.416002 | *"Good alignment -- domain match on retrieval/ranking: the Google experience puts this in the JD's sweet spot rather than at the margins."* | | 58 | `CAND_0073675` | 0.414474 | *"Solid fit -- Ola context is described with vocabulary of someone who codes and ships rather than delegates -- a sound IC signal for a role that values engineering execution over org-chart seniority."* | | 59 | `CAND_0040117` | 0.414383 | *"Solid fit -- PhonePe shows up as a context where ML went to production, not just to a notebook -- the description puts this in a well-rounded tier for what the role is actually asking for."* | | 60 | `CAND_0036863` | 0.412898 | *"A fair match -- Wysa context is described with vocabulary of someone who codes and ships rather than delegates -- a strong IC signal for a role that values engineering execution over org-chart seniority. Worth flagging: the tenure curve here -- 6 months at the shortest, 17 on average -- puts the hop rate at 67% and is the main thing standing between a strong profile and an easy yes."* | | 61 | `CAND_0068964` | 0.412740 | *"Solid fit -- product-company background at Mad Street Den (AI/ML) -- the JD explicitly screens for this context, and a strong fraction of this career is product-company tenure. That said, job-hopping is a real concern -- average tenure runs around 28 months per role, a 50% hop rate."* | | 62 | `CAND_0006418` | 0.408773 | *"Solid fit -- Flipkart shows up as a context where ML went to production, not just to a notebook -- the description puts this in a well-rounded tier for what the role is actually asking for."* | | 63 | `CAND_0052682` | 0.408515 | *"Solid fit -- evaluation framework usage in evidence at Aganitha: the role description references ranking metrics (NDCG, MRR, or similar), which is the rigour signal the JD is hiring for."* | | 64 | `CAND_0075439` | 0.404030 | *"Workable match -- the work at Flipkart is described in the language of someone who built things themselves: a dependable hands-on engineering signal, not an architectural or managerial one."* | | 65 | `CAND_0015528` | 0.398984 | *"Solid fit -- Krutrim context is described with vocabulary of someone who codes and ships rather than delegates -- a solid IC signal for a role that values engineering execution over org-chart seniority."* | | 66 | `CAND_0077337` | 0.397726 | *"Solid fit -- shipping ML to real users is the hardest signal to fake on a resume, and Razorpay's context makes a blue-chip case that this candidate has actually done it. Weighed against that, history shows 50% of roles closing inside 18 months (averaging 21 months) -- a pattern that deserves a straight question in the loop, not a quiet pass."* | | 67 | `CAND_0036184` | 0.397416 | *"Reasonable case -- consistently at product companies -- CRED (Fintech) is the kind of org this JD targets, and the tenure there is a well-rounded match for what's being asked."* | | 68 | `CAND_0017590` | 0.395675 | *"Reasonable case -- Genpact AI shows up as a context where ML went to production, not just to a notebook -- the description puts this in a well-rounded tier for what the role is actually asking for. On the downside, a 50% hop rate is a pattern that calls for a direct retention conversation, rather than an assumption that the next stop will be a long one."* | | 69 | `CAND_0020708` | 0.395627 | *"Reasonable case -- at PolicyBazaar, this person was writing code and shipping systems rather than directing or reviewing -- a dependable execution track record for a role that requires IC depth."* | | 70 | `CAND_0058688` | 0.395114 | *"Workable match -- domain match on retrieval/ranking: the Apple experience puts this in the JD's sweet spot rather than at the margins. That said, based in Germany, outside India, with no sponsorship path on file -- a logistics blocker independent of skill fit."* | | 71 | `CAND_0041669` | 0.394665 | *"Workable match -- product-company background at CRED (Fintech) -- the JD explicitly screens for this context, and a marquee fraction of this career is product-company tenure."* | | 72 | `CAND_0018549` | 0.394459 | *"Solid fit -- a well-grounded production ML signal from Flipkart: not just model training but live inference with the operational responsibility that comes with it."* | | 73 | `CAND_0075574` | 0.394358 | *"Solid fit -- hybrid retrieval technology visible in the skills: BM25 -- a competitive alignment with the JD's hybrid-retrieval requirement."* | | 74 | `CAND_0098846` | 0.393851 | *"Passable fit -- upGrad shows up as a retrieval/ranking context -- exactly the core domain this role requires; a well-grounded case for genuine domain depth rather than keyword proximity."* | | 75 | `CAND_0070333` | 0.393534 | *"Passable fit -- PhonePe (Fintech) is a product-company context; the career has spent an above-average portion of its time building for users, not billing clients."* | | 76 | `CAND_0082086` | 0.392451 | *"Good alignment -- Razorpay context is described with vocabulary of someone who codes and ships rather than delegates -- an above-average IC signal for a role that values engineering execution over org-chart seniority."* | | 77 | `CAND_0006538` | 0.391200 | *"Good alignment -- the JD's primary bar is production ML; the Dream11 context in this profile clears it -- the work is framed in deployment terms, not research terms."* | | 78 | `CAND_0048534` | 0.388814 | *"Good alignment -- product-company background at Genpact AI (AI Services) -- the JD explicitly screens for this context, and a competitive fraction of this career is product-company tenure."* | | 79 | `CAND_0006209` | 0.388281 | *"Reasonable case -- the work at Aganitha is described in the language of someone who built things themselves: a sound hands-on engineering signal, not an architectural or managerial one."* | | 80 | `CAND_0084681` | 0.386574 | *"Passable fit -- core domain evidence at Zomato -- ranking and retrieval work in a production context; a dependable fit for this role's most specific technical requirement."* | | 81 | `CAND_0067866` | 0.385921 | *"A fair match -- Freshworks (SaaS) is a product-company context; the career has spent an above-average portion of its time building for users, not billing clients."* | | 82 | `CAND_0078262` | 0.385901 | *"A fair match -- hybrid retrieval technology visible in the skills: FAISS -- a strong alignment with the JD's hybrid-retrieval requirement. The one caveat: the pattern across this career history -- 50% hop rate, 29-month average stay -- reads as title-chasing risk rather than settled progression."* | | 83 | `CAND_0054703` | 0.385780 | *"Solid fit -- hands-on engineering at Razorpay: the description uses builder language -- shipped, built, deployed -- not just 'oversaw' or 'designed'; a strong IC execution signal for a role that needs exactly this. Worth flagging: job-hopping is a real concern -- average tenure runs around 20 months per role, a 50% hop rate."* | | 84 | `CAND_0046525` | 0.385091 | *"Reasonable case -- Information Retrieval in the skills section is exactly the evaluation-metric literacy the JD flags as a differentiator; a capable credibility signal for a ranking role."* | | 85 | `CAND_0060257` | 0.383732 | *"Passable fit -- the JD's primary bar is production ML; the Paytm context in this profile clears it -- the work is framed in deployment terms, not research terms."* | | 86 | `CAND_0012837` | 0.382973 | *"Good alignment -- production ML track record at Sarvam AI: the work described goes beyond experimentation into live systems with real-user stakes -- a competitive signal for this role's primary bar."* | | 87 | `CAND_0034177` | 0.380854 | *"Workable match -- Mad Street Den (AI/ML) is a product-company context; the career has spent a respectable portion of its time building for users, not billing clients."* | | 88 | `CAND_0011327` | 0.380681 | *"Passable fit -- Krutrim shows up as a context where ML went to production, not just to a notebook -- the description puts this in a sound tier for what the role is actually asking for. Even so, tenure stability is the soft spot here: 67% of roles under 18 months, averaging 25 months apiece, which tempers the otherwise positive read of this profile."* | | 89 | `CAND_0042029` | 0.380019 | *"A fair match -- evaluation framework usage in evidence at Flipkart: the role description references ranking metrics (NDCG, MRR, or similar), which is the rigour signal the JD is hiring for."* | | 90 | `CAND_0017648` | 0.379223 | *"A fair match -- at Freshworks, this person was writing code and shipping systems rather than directing or reviewing -- a well-grounded execution track record for a role that requires IC depth. Worth flagging: 16 months is the average stay here, and at a 67% hop rate this looks more like a pattern than a one-off job change."* | | 91 | `CAND_0038099` | 0.379216 | *"Reasonable case -- BM25 in the skills section is the JD's hybrid-retrieval signal in concrete form; a robust match for this specific technical ask. Worth flagging: notice period runs 90 days, well past the 30-day window the role would prefer -- a real but manageable logistics gap."* | | 92 | `CAND_0098952` | 0.378712 | *"Reasonable case -- CRED context is described with vocabulary of someone who codes and ships rather than delegates -- a robust IC signal for a role that values engineering execution over org-chart seniority."* | | 93 | `CAND_0081321` | 0.378303 | *"Good alignment -- the JD is written for someone who has shipped to end users inside a product company; Freshworks (SaaS) is exactly that context -- an outstanding fit on this screen. Even so, at 120 days, notice runs squarely into 'over 30 higher bar' territory -- not a blocker, but a start-date conversation that needs to happen early rather than after an offer is out."* | | 94 | `CAND_0024203` | 0.378048 | *"Passable fit -- InMobi shows up as a context where ML went to production, not just to a notebook -- the description puts this in a well-rounded tier for what the role is actually asking for. On the downside, 50% of recent roles ran under 18 months -- a retention risk the strengths above don't erase on their own."* | | 95 | `CAND_0081053` | 0.377870 | *"Reasonable case -- Glance (AI/ML) is a product-company context; the career has spent an above-average portion of its time building for users, not billing clients. Still, notice period (90 days, 'over 30 higher bar') is the practical catch here -- everything upstream of the offer stage looks clean."* | | 96 | `CAND_0064904` | 0.377837 | *"A fair match -- core domain evidence at Freshworks -- ranking and retrieval work in a production context; an above-average fit for this role's most specific technical requirement. Set against that, the start-date math is the friction point: 90 days of notice classifies as 'over 30 higher bar', which is worth flagging to whoever is planning the onboarding calendar."* | | 97 | `CAND_0007874` | 0.377229 | *"Reasonable case -- Sarvam AI shows up as a context where ML went to production, not just to a notebook -- the description puts this in a well-rounded tier for what the role is actually asking for."* | | 98 | `CAND_0070589` | 0.376549 | *"Good alignment -- production ML track record at Meesho: the work described goes beyond experimentation into live systems with real-user stakes -- a competitive signal for this role's primary bar."* | | 99 | `CAND_0044753` | 0.376543 | *"Solid fit -- most recently at product companies -- Flipkart (E-commerce) is the kind of org this JD targets, and the tenure there is a sound match for what's being asked. Worth flagging: a 50% hop rate is a pattern that calls for a direct retention conversation, rather than an assumption that the next stop will be a long one."* | | 100 | `CAND_0050876` | 0.376483 | *"Reasonable case -- hybrid retrieval technology visible in the skills: FAISS -- a strong alignment with the JD's hybrid-retrieval requirement. On the downside, notice period (90 days, 'over 30 higher bar') is the practical catch here -- everything upstream of the offer stage looks clean."* |