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Sasi Katamneni
Mid-level Data Scientist / AI-ML Engineer specializing in Generative AI and LLM applications
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Built a production GenAI-powered analytics assistant to reduce reliance on data analysts by enabling natural-language Q&A over Databricks/Power BI dashboards, backed by vector search (Pinecone/Milvus) and a Neo4j knowledge graph, including multimodal support via OpenAI Vision. Demonstrates strong real-world LLM reliability engineering with strict RAG, LangGraph multi-step verification, and Guardrails/custom validators, plus broad orchestration and production monitoring experience (Airflow, ADF, Step Functions, Kubernetes, Prometheus/CloudWatch).
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Key Strengths
- Built and deployed a production GenAI analytics assistant integrating Databricks/Power BI, LLM agents, vector search, and a Neo4j knowledge graph
- Strong hallucination-mitigation approach (strict RAG, multi-step verification with LangGraph, Guardrails AI + custom validators)
- End-to-end orchestration experience across Airflow, Azure Data Factory, AWS Step Functions, Kubernetes, and LLM workflow orchestration
- Production reliability practices: versioned DAGs, CI/CD, containerization, retries, health checks, monitoring/alerting (Prometheus, CloudWatch)
- Structured agent/workflow testing and evaluation using unit tests, synthetic edge cases, scenario evals, and metrics (grounding, latency, failure modes)
- Effective collaboration with non-technical stakeholders (fraud investigation team) to incorporate domain rules and reduce manual review workload
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