Junior Machine Learning Engineer specializing in LLMs, RAG, and medical imaging
New York City, USAMachine Learning Researcher, Non Traditional Volunteer3 years experienceJuniorHealthcareHealthcare ITLegal
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About
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.
Experience
Machine Learning Researcher, Non Traditional VolunteerNYU Langone Health
Machine Learning Engineer InternFileread AI
Visting Researcher, JUACEP Fellowship awardeeFujii Lab, Nagoya University
Research AssistantSikkim Manipal Institute of Technology
Education
New York Universitymaster, Computer Engineering (2025)
Sikkim Manipal Universitybachelor, Computer Science and Engineering (Hons. AI) (2023)
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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