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Mehul Parmar
Mid-level Data Scientist specializing in insurance, healthcare, and cloud analytics
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Built a production-style LLM document summarization/generation workflow that mitigates token limits and reduces hallucinations using semantic chunking, FAISS-based embedding retrieval (top-k via cosine similarity), and section-wise generation. Orchestrated the end-to-end pipeline with AWS Step Functions and aligned outputs with sales stakeholders through demos, visuals, and documentation.
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Key Strengths
- Designed and implemented RAG pipeline using semantic chunking + FAISS to handle token limits
- Section-wise generation approach to produce complete outputs from large user requirements
- Embedding-based retrieval with cosine similarity and top-k selection for relevance
- Orchestrated end-to-end LLM document workflow with AWS Step Functions (upload → chunk → embed → retrieve → generate → merge)
- Reliability focus via grounding, clear step breakdown with schemas, and evaluation matrix with human verification
- Effective collaboration with non-technical stakeholders using demos, visuals, feedback loops, and documentation
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