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revanth polthi
Mid-level Data Scientist specializing in Generative AI and MLOps
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GenAI/LLM engineer with production experience at Allstate building an end-to-end document intelligence workflow for insurance operations—automating document intake, classification, and risk signal extraction. Emphasizes high-reliability design for regulated/high-stakes outputs using schema enforcement, confidence thresholds, validation rules, and human-in-the-loop routing, with metric-driven offline evaluation and production monitoring.
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Key Strengths
- Built and deployed a GenAI document intelligence system in production for insurance operations (intake, classification, risk extraction, downstream routing)
- Reliability-focused LLM system design: separated generation from business logic; added validation rules, confidence thresholds, and schema checks to reduce hallucination risk
- Designed modular, swappable multi-step LLM workflows using LangChain (models/prompts/retrieval logic interchangeable without breaking pipeline)
- Implemented human-in-the-loop fallbacks for low-confidence cases in high-stakes workflows
- Metric-driven evaluation approach (retrieval quality, extraction accuracy, downstream outcomes) with offline testing on curated datasets and edge cases plus production monitoring for drift/latency
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