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Sai Macherla
Mid-level Full-Stack Java Developer specializing in Healthcare and Financial Services AI
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Built and shipped production LLM/RAG systems at Mayo Clinic, including a conversational AI assistant for patient pre-consultation and a clinical-trial matching tool for doctors. Implemented HIPAA-compliant de-identification and guardrails, plus real-time feedback logging and fine-tuning that improved response accuracy by 15% and reduced admin workload by 25%.
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Key Strengths
- Shipped production conversational AI assistant end-to-end (MVP → launch → iteration) in healthcare setting
- Implemented RAG pipeline with Pinecone for fast/accurate clinical note retrieval
- Built real-time feedback logging and iterative fine-tuning loop; improved response accuracy by 15%
- Reduced administrative workload by 25% through automation
- Designed LLM guardrails: de-identification, HIPAA-compliant storage/processing, encryption, access controls, low-confidence flagging for review
- Ran LLM evaluation loops using real user questions; used eval results to improve retrieval logic and increase answer quality
- Built scalable data pipeline for messy data using Python + Kafka with monitoring and batch processing for cost/latency balance
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