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Ravali Yerrapothu
Senior Machine Learning Engineer specializing in LLMs, RAG, and Computer Vision
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About
Built a production LLM-powered clinical note summarization and retrieval system that structures patient/provider/payer discussions into standardized outputs (symptoms, treatments, clinical codes, and prior-auth decisions) and stores notes as embeddings for hybrid search and proactive prior-authorization prediction. Experienced with LangChain/LangGraph orchestration, RAG, and grounding against medical code databases, and has communicated model feasibility/limitations to business stakeholders (Virtusa/Comcast).
Experience
Education
Key Strengths
- Built and deployed an LLM-powered clinical note summarization system for patient/provider/payer conversations
- Designed hybrid retrieval (keyword + semantic) to resolve discrepancies and improve medical-code-precise retrieval
- Grounded outputs against a medical code database to reduce terminology/coding mistakes
- Hands-on orchestration with LangChain (tokenization, templates, RAG/search criteria) and LangGraph (agent-based summarization/Q&A)
- Pragmatic evaluation approach using human review and repeated-input consistency testing
- Able to align non-technical stakeholder expectations with data/feasibility constraints (Virtusa/Comcast sales prediction use case)
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