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Saivedant Hava
Entry AI Engineer specializing in LLMs, RAG, and MLOps
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About
Built and shipped a production Python-based agentic RAG document retrieval system over 80K records using FastAPI, OCR, vector search, and AWS infrastructure, with a strong emphasis on reliability, testing, and observability. Stands out for treating AI failures like production incidents—turning hallucinations, retrieval misses, and OCR issues into regression tests—and for quantifiably reducing document lookup time from about 12 minutes to under 90 seconds.
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Key Strengths
- End-to-end production RAG integration in Python
- Reliability engineering for AI/document retrieval systems
- Multi-layer testing strategy across unit, integration, and regression
- Strong observability and monitoring with Prometheus and OpenTelemetry
- AI safety focus including PII scanning and hallucination guardrails
- Pragmatic UI automation for dynamic dashboards using Playwright
- Secure handling of MFA/CAPTCHA via staging accounts and pre-authenticated sessions
- Ambiguous process automation with clear edge-case modeling
- Reduced document lookup time from ~12 minutes to under 90 seconds
- Experience operating document retrieval at 80K-record scale
Reference Highlights
- owned the entire customer-facing frontend
- high quality customer-facing work
- ships quickly
- clear upfront scoping
- strong follow-through
- direct customer engagement
- turns feedback into changes efficiently
- measurable implementation results
- adaptable when specs change
- high performer
- clean and organized code
- strong production engineering
- reliable production systems
- built low-latency customer-facing views
- reliable automation
- clear error handling
- effective end-to-end test coverage
- built robust data fallbacks
- great communication
- strong partner to work with
- translates requirements into a clean product
- proactively flags risks
- provides realistic timelines
- simplifies technical concepts for non-technical stakeholders
- rare combination of scoping, timeliness, and risk flagging
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