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Chethan Thimapuram
Mid-level AI Engineer specializing in LLMs, MLOps, and healthcare NLP
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About
Built a production, real-time clinical documentation system at HCA that converts doctor–patient conversations into structured clinical summaries using speech-to-text, LLM summarization, and RAG. Demonstrated measurable gains from medical-domain fine-tuning (clinical concept recall +18%, ROUGE-L 0.62 to 0.74) while meeting HIPAA constraints via PHI anonymization and encryption, and deployed via Docker/FastAPI with CI/CD and monitoring.
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Key Strengths
- Built and deployed real-time clinical documentation system (STT + LLM) in production
- HIPAA/PHI privacy-by-design approach (anonymization + encryption)
- Quantified model improvements via fine-tuning (clinical concept recall +18%; ROUGE-L 0.62→0.74)
- Designed modular LLM workflows (retrieval/reasoning/generation) with unit + end-to-end evaluation
- Implemented RAG with embeddings + vector database for clinical knowledge retrieval
- Production reliability focus: low-latency, high-uptime deployment with Docker/cloud and CI/CD
- Effective collaboration with non-technical clinical stakeholders using demos and iterative feedback
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