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Nagasaikumar Jampani
Mid-level AI/ML Engineer specializing in Generative AI and RAG pipelines
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About
AI/LLM engineer with healthcare domain experience who built a production clinical support “chart bot” for Molina, including PHI-safe ingestion of 200k+ PDF policies, vector retrieval, and a fine-tuned LLaMA served via vLLM on ECS Fargate. Demonstrated measurable performance wins (HNSW + namespace partitioning; 30% inference latency reduction) and a rigorous evaluation/monitoring approach, while partnering closely with nurses and operations teams to shape workflows and guardrails.
Experience
Education
Key Strengths
- Built and deployed end-to-end clinical RAG system (ingestion → retrieval → inference) for nurses searching large policy corpora
- Scaled retrieval performance on 200k+ PDFs using HNSW indexing, metadata filters, and domain-specific namespaces
- Reduced and stabilized inference latency by migrating to vLLM with improved GPU allocation and batching (30% latency reduction)
- Strong workflow orchestration across Airflow, AWS Step Functions, and GitHub Actions for ingestion, embedding jobs, and CI/CD
- Structured reliability approach for AI agents: component testing, Recall/Precision@K, scenario evals, and automated LLM-judge scoring
- Effective collaboration with non-technical healthcare stakeholders (nurses/ops) using prototypes and iterative feedback
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