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Pranav Puranik
Senior AI Engineer specializing in LLMs, RAG, and multimodal NLP
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About
Built a production LLM/RAG assistant for insurance/health claims agents that ingests 100–200 page patient PDFs via OCR (migrated from local Tesseract to Azure Document Intelligence) and delivers grounded claim detail retrieval plus summaries with PII/PHI guardrails. Experienced orchestrating large workflows with Celery worker pipelines and AWS Step Functions (S3-triggered, Fargate-based batch inference/accuracy aggregation), and collaborates closely with non-technical SMEs (claims agents/nurses) through shadowing, iterative demos, and SME-defined evaluation.
Experience
Education
Key Strengths
- Built and deployed a production claims-agent LLM assistant for long medical/claims PDFs (OCR → chunking → retrieval → summaries)
- Designed HIPAA-conscious guardrails (PII/PHI removal, disclaimers, grounding to patient-specific data) to reduce hallucinations
- Pragmatic retrieval strategy for hard-to-embed medical codes (hybrid search + ICD lookup approach)
- Scaled document processing with async worker pipeline (Celery) and cloud OCR (Azure Document Intelligence)
- Strong orchestration experience across AWS Step Functions/S3/Fargate and Dagster for data pipelines
- SME-driven product iteration and evaluation (shadowing users, iterative demos, SME-authored test cases, relevance/retrieval metrics)
- Applied structured output validation (Pydantic) and log-driven prompt iteration
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