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Neeraj Jawahirani
Mid-level Data & AI Engineer specializing in healthcare data pipelines and MLOps
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About
Built and deployed a production LLM-powered clinical note summarization system used by care managers to speed review of 5–20 page unstructured medical records. Implemented safety-focused validation (prompt constraints, rule-based and section-level checks, human-in-the-loop) to reduce hallucinations while maintaining low latency and meeting privacy/regulatory constraints, integrating via APIs into existing clinical tools.
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Key Strengths
- Built and deployed production LLM clinical note summarization for care managers
- Designed safety/accuracy controls (prompt constraints, rule-based checks, section-level validation) to reduce hallucinations
- Handled large-document constraints via chunking and relevance filtering with context limits
- Balanced clinical workflow needs with low-latency, reliable service delivery
- Strong evaluation discipline: success metrics, curated eval sets (edge/failure cases), automated validation and integration tests
- Production reliability practices: monitoring, canary releases, feedback loops to catch regressions
- Pragmatic orchestration: used LangChain plus custom Python for better observability and fine-grained control
- Effective collaboration with non-technical clinical stakeholders to translate needs into requirements and iterate based on feedback
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