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Shamala Chandrappa
Mid-level AI Engineer specializing in agentic AI, LLM systems, and healthcare AI
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About
Healthcare-focused ML/AI engineer who has built production voice agents and clinical question-answering systems end-to-end, from experimentation through deployment, observability, and iteration. Particularly strong in making LLM systems reliable in real workflows via RAG, fine-tuning, guardrails, evaluation pipelines, and shared Python tooling; cites ~20% clinical QA accuracy gains and ~40% faster physician decision turnaround.
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Key Strengths
- owned production healthcare voice AI systems end-to-end
- strong LLM/agent evaluation and observability design
- translated agentic AI research into reliable production workflows
- deep healthcare GenAI and clinical QA experience
- combined fine-tuning and RAG for grounded clinical answers
- improved clinical Q&A accuracy by around 20%
- reduced physician decision turnaround time by around 40%
- iterative production optimization using real conversation failure analysis
- balanced latency, cost, quality, and safety tradeoffs in real-time AI systems
- built reusable Python evaluation services that improved team velocity
- established shared metrics across agents for regression detection and reliability
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