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Radhika Mangroliya
Senior Data Scientist / AI Engineer specializing in LLMs, RAG, and production ML
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About
Data science professional who has built a production RAG-based LLM question-answering system ("Flash Query") to deliver fast, accurate answers over large document collections, focusing on retrieval quality and grounded responses. Also collaborates with non-technical retail/jewelry stakeholders to turn business questions into predictive models and dashboards for decision-making.
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Key Strengths
- Built and deployed a production LLM-based QA + information retrieval system using a RAG pipeline
- Improved retrieval accuracy through chunking strategy and prompt design iterations
- Structured testing approach using sample question sets and evaluation of relevance/answer accuracy
- Hands-on orchestration of end-to-end LLM pipelines with LangChain (document loading, embeddings, retrieval, generation)
- Designs agent/workflow evaluation with measurable criteria (accuracy, relevance, latency) and grounding checks to reduce hallucinations
- Translates business needs into ML problems and communicates results via dashboards for non-technical stakeholders
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