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Utkarsh Srivastava
Junior Machine Learning Engineer specializing in LLMs, RAG, and medical imaging
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At Fileread, the candidate built and deployed an LLM-powered legal document classification and retrieval layer for an agentic extraction system that turns unstructured legal PDFs into structured tables with line-level citations. They productionized a RAG-style pipeline (ingestion, embeddings, retrieval, reranking, generation) and report 95%+ F1 across 70+ legal categories, emphasizing rigorous evaluation and close collaboration with legal domain experts for high-stakes precision.
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Key Strengths
- Built and productionized LLM-powered legal document classification/retrieval pipeline
- Achieved 95%+ F1 across 70+ legal categories on a large labeled dataset
- Designed agentic extraction system converting legal PDFs into structured tables with line-level citations
- Structured prompt iteration using constrained templates and fixed evaluation query sets
- Reliability-focused agent design with explicit, observable workflow steps
- Evaluation-driven approach using grounding accuracy, citation precision, latency, and fallback rates
- Pragmatic model/retrieval selection: start with baselines and add complexity (e.g., reranking/structured prompts) based on measured failure modes
- Effective cross-functional collaboration with legal domain experts to define success criteria and acceptable failure cases
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Experience
Education
Awards
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Publications
3 publications
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