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Ambuk Rehani
Mid-level AI/Backend Engineer specializing in RAG and data platforms
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Built and shipped a production LLM-powered financial Q&A interface that extracts precise numeric data from PDFs using a hybrid AWS Textract + LLM normalization pipeline, with confidence gating and guardrails to prevent unreliable answers. Experienced with LangChain-based RAG orchestration (chunking, memory, structured outputs) and collaborated closely with PMs/analysts on IRS Form 990 extraction requirements.
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Key Strengths
- Built and deployed production LLM financial Q&A system with PDF/table extraction
- Hybrid extraction approach (AWS Textract + LLM normalization) to improve numeric accuracy
- Reliability engineering for LLM apps: confidence gates using retrieval similarity + Textract field/cell confidence
- Implemented guardrails (blocked/softened answers when context weak) to reduce hallucinations
- Cost/performance controls in production (prompt limits, caching repeat queries)
- Strong RAG and orchestration implementation using LangChain (chunking, memory, structured outputs)
- Effective translation of non-technical stakeholder needs into extraction/prompt requirements (IRS Form 990 use cases)
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