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Kunal Kulkarni
Intern AI/ML Researcher specializing in computer vision and data engineering
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About
Built a production-oriented multimodal RAG "Fix Assistant" with FastAPI, Tavily search, BM25 + cross-encoder reranking, and a local Phi-3.5 model, emphasizing strict grounding and fallback/verification modes to prevent hallucinations. Also has hands-on federated learning experience using STADLE to orchestrate edge-node training and aggregation for EV telemetry data, plus experience communicating AI results to non-technical stakeholders (traffic RL/congestion outcomes).
Experience
Education
Key Strengths
- Built end-to-end RAG troubleshooting assistant (retrieval + reranking + local LLM + orchestration)
- Strong reliability engineering for LLM apps (strict grounding, templates, citation/contradiction checks, fallback chains)
- Practical evaluation discipline (unit tests, offline eval on logs, controlled rollouts, baselines, ablations)
- Federated/distributed ML orchestration on edge nodes using STADLE
- Improved federated training stability via stratified partitioning and weighted aggregation using node validation metrics
- Able to translate technical KPIs into stakeholder-facing outcomes (e.g., congestion reduction in traffic RL project)
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