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Ashtik Mahapatra
Mid-level Data Scientist specializing in LLMs, RAG, and document intelligence
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About
LLM/ML engineer who has shipped production systems in legal/financial-risk domains at Wolters Kluwer, including a hybrid OCR+deterministic+LLM extraction pipeline that structured UCC filings at massive scale and drove $6M+ in revenue. Also built LangGraph-based multi-agent “Deep Research” workflows with model routing, tool calls (MCP), persistence, and human-in-the-loop review, and partnered closely with policy writers to deliver LLM summarization that cut writing time by ~60%.
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Key Strengths
- Built and deployed production LLM+ML system extracting structured data from messy legal documents (scanned PDFs/free-form text)
- Designed cost/latency-aware hybrid pipeline (deterministic + OCR + LLM fallback with caching) to scale reliably
- Scaled entity resolution across millions of filings using blocking and fuzzy matching to balance recall vs false positives
- Delivered measurable business impact ($6M+ revenue attributed to production system)
- Built deterministic multi-agent workflows in LangGraph with bounded loops, state passing, failure handling, and human-in-the-loop review
- Strong evaluation discipline for agents (schema validity, tool-call success, hallucination rate, latency; golden sets + LLM judge + periodic human validation)
- Effective cross-functional collaboration with non-technical stakeholders to define quality criteria and create feedback loops for fine-tuning data
- Improved policy-writer productivity via LLM summarization (20–30% publishable; 50% with light edits; ~60% time reduction)
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