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Prachi Jain
Mid-level Machine Learning Engineer specializing in NLP, LLMs, and MLOps
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About
Built and productionized a RAG-based analytics Q&A assistant for a financial analytics team, enabling natural-language querying across 200+ datasets (SQL tables, PDFs, compliance docs, wikis) and cutting turnaround time by 60%. Deep experience delivering regulated, audit-ready LLM systems on Azure (Azure OpenAI + LangChain) with strict grounding/citations, hybrid retrieval, and AKS-based low-latency deployment, plus strong collaboration with compliance analysts and auditors via iterative Gradio demos.
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Key Strengths
- Built and deployed production RAG analytics assistant across 200+ complex financial datasets
- Reduced insight turnaround time by 60% and saved 10+ hours/week via natural-language analytics Q&A
- Strong hallucination control/grounding in regulated domains using retrieval-first prompting and mandatory citations
- Designed low-latency retrieval at scale using hybrid search, embedding/chunking tuning, caching, and AKS containerization
- End-to-end LLM workflow orchestration with LangChain (routing, retrieval, SQL tools, agent/tool decisions, structured outputs)
- Rigorous evaluation approach: golden query sets, unit tests, offline batch evals (accuracy/precision@k/latency/cost) and production monitoring
- Effective cross-functional delivery with non-technical compliance/audit stakeholders using iterative Gradio prototypes and acceptance criteria
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