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Adarsh Vodnala
Mid-level Full-Stack Software Engineer specializing in SaaS and AI-enabled platforms
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About
Built and shipped production AI features in the automotive dealership domain, including an end-to-end computer-vision damage detection system for trade-ins and a tool-calling, RAG-enabled LotSync AI Agent that answers inventory/VIN questions using strict schemas and internal APIs to avoid hallucinations. Also developed a Dagster + Oracle automated reporting pipeline as a Graduate Research Assistant, supporting 15+ university departments with normalized, reliable ETL workflows.
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Key Strengths
- Owned and shipped AI-driven vehicle damage detection end-to-end (photo upload → vision models → repair cost estimation → valuation)
- Balanced accuracy vs response time via rapid initial response with deeper follow-up analysis
- Improved AI transparency post-launch by adding repair types/cost breakdown and selectable repair options
- Built production LLM agent using tool-calling with strict schemas to prevent hallucinations and ensure responses come from internal APIs
- Implemented guardrails: parameter validation, conversation state management, logging, and tool-execution fallbacks
- Designed LLM eval loop with dealership query test set; improved tool selection by refining tool descriptions and schemas
- Built scalable ETL/reporting pipeline on Oracle using Dagster with normalization, data quality checks, retries/backoff, and Dockerized deployment
- Scaled reporting via batch scheduling, indexing, and caching; supported 15+ university departments
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