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venkata Kommineni
Senior AI/ML Engineer specializing in Generative AI, agentic systems, and RAG
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About
Built and deployed an agentic RAG assistant in production to automate enterprise knowledge search and multi-step workflows with tool calling, tackling real-world issues like hallucinations, retrieval accuracy, and latency. Demonstrates strong LLMOps and orchestration depth (MLflow, Airflow, LangGraph/LangChain/LlamaIndex) plus a metrics-driven approach to agent testing/evaluation and cross-functional delivery with business stakeholders.
Experience
Education
Key Strengths
- Deployed production agentic RAG assistant for enterprise knowledge search and multi-step workflows
- End-to-end approach to reducing hallucinations via RAG grounding, retrieval tuning, and prompt guardrails
- Performance optimization for multi-step LLM systems using caching, batching, and latency decomposition
- Strong orchestration experience across data/ML and agent workflows (Airflow, LangChain/LangGraph/LlamaIndex)
- Reliability-focused agent design with constrained action space and staged workflow architecture
- Measurement-driven evaluation and layered testing (unit/integration/regression/load) with clear agent quality metrics
- Effective translation of business workflows into AI use cases through stakeholder discovery and expectation-setting
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