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Karthik Gantasala
Mid-level Generative AI Engineer specializing in LLM agents and RAG
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GenAI/LLM engineer who built and deployed a production RAG system for enterprise document search and decision support, cutting manual lookup time by 40%+. Experienced with LangChain/LangGraph agent orchestration plus Airflow/Prefect for ingestion and incremental reindexing, with a strong focus on reliability (testing, observability) and stakeholder-driven metrics.
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Key Strengths
- Built and deployed a production LLM-powered RAG system for enterprise knowledge retrieval/decision support
- Reduced manual document lookup time by 40%+
- Strong hallucination mitigation via strict context grounding, citations, and similarity thresholds
- Performance and scale optimization (chunk sizing, caching frequent queries, parallel retrieval, async APIs, auto-scaling)
- Designed data freshness approach with incremental reindexing pipelines
- Hands-on orchestration across GenAI and data/ML workflows (LangChain, LangGraph, Airflow, Prefect)
- Reliability-focused agent/workflow design with modular components and layered testing (unit tests, mocked LLMs, eval datasets, failure scenarios)
- Stakeholder collaboration using early metric alignment, demos/prototypes, and acceptance testing
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