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Jahnavi Chakka
Mid-level Machine Learning Engineer specializing in LLMs, NLP, and MLOps
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About
Built a production LLM-RAG system at McKesson to let internal healthcare operations teams query large volumes of unstructured operational documents via natural language with source-backed answers, designed with HIPAA/FHIR compliance in mind. Demonstrated strong production engineering across hallucination mitigation, retrieval quality tuning, and latency/scalability optimization, using LangChain/LangGraph and Airflow plus rigorous evaluation/monitoring practices.
Experience
Education
Key Strengths
- Built and deployed a production LLM-RAG system for healthcare document/data querying
- Strong hallucination control via prompt constraints and retrieval-only grounding
- Improved retrieval relevance through chunking/overlap tuning and top-k + similarity thresholding validated on real user queries
- Performance optimization for scale (batch embeddings, caching, vector index tuning)
- End-to-end LLM workflow orchestration using LangChain and LangGraph
- Reliability-focused agent/workflow design with modular components, guardrails, and confidence thresholds
- Structured testing/evaluation approach (golden Q&A sets, unit/integration/edge tests; metrics incl. precision/recall, latency, cost, failure rates)
- Effective collaboration with non-technical stakeholders by defining success metrics and iterating via demos (accuracy, latency, 'I don’t know' behavior)
- Healthcare compliance awareness (HIPAA/FHIR) in AI solution design
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