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Yashkumar Patel
Mid-level Software Engineer specializing in backend, distributed systems, and AI infrastructure
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Built Baioniq, an enterprise LLM platform for automating extraction from massive unstructured documents like contracts and insurance claims. They demonstrate unusually strong production depth in agentic AI—scaling to 100k+ requests/day, processing 1M+ claim documents, and improving extraction accuracy through rigorous RAG architecture, evaluation, and fallback design.
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Key Strengths
- Built and shipped a production LLM document-processing platform at enterprise scale
- Handled 100k+ requests/day with enterprise-grade reliability
- Processed over 1 million insurance claim documents
- Achieved 95% extraction accuracy in production
- Strong RAG architecture design for long, unstructured documents
- Reduced latency by 35% with Redis semantic caching
- Designed predictable multi-step agent workflows with strict structured outputs
- Implemented deterministic fallbacks and human-in-the-loop review for low-confidence cases
- Deep expertise in hallucination mitigation via parent-child retrieval
- Built rigorous evaluation framework with golden datasets, LLM-as-a-judge, and Recall@K
- Improved financial extraction accuracy from 82% to 97% through targeted error analysis
- Designed validation-first ingestion and idempotent processing for messy enterprise data
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