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Venkatesh Nagubandi
Mid-level Software Engineer specializing in ML, LLM apps, and cloud data systems
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Built a production SQL chatbot for access-log analytics that replaced manual custom report requests with natural-language querying, using LangGraph and a ChromaDB-backed RAG pipeline for grounded, consistent answers. Implemented a privacy-preserving design where the LLM never sees raw customer data (only query metadata) and has experience building multi-agent/tool-calling systems with LangGraph (DeepAgents), including solving sub-agent communication drift via self-reflection.
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Key Strengths
- Built and deployed an LLM-powered SQL chatbot for access log analytics/reporting
- Designed privacy-preserving architecture where the LLM only sees metadata (not raw data)
- Implemented RAG with ChromaDB to ground responses in documentation and improve consistency
- Experience building agentic LangGraph systems with tool-calling loops (DeepAgents)
- Solved multi-agent communication drift using a self-reflection tool
- Pragmatic evaluation approach: scoped inputs/outputs, automated tests, LLM-as-judge metrics, plus human testing
- Cost/performance-aware retrieval and model selection (BM25 + embeddings; start with mid-sized models, scale up as needed)
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