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Sasi Katamneni

Mid-level Data Scientist / AI-ML Engineer specializing in Generative AI and LLM applications

Dallas, TXSoftware Engineer AI/ML5 years experienceMid-LevelHealthcareHealthcare ITE-commerce
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About

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).

Experience

Software Engineer AI/MLBaylor Scott & White
Data ScientistMeesho
Data ScientistVistara

Education

University of North Texasmaster, Data Science
Amrita Viswa Vidyapeethambachelor, Computer Science

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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Languages

English

Skills

A/B TestingAgileAjaxAlteryxAmazon API GatewayAmazon AthenaAmazon BedrockAmazon CloudWatchAmazon EC2Amazon EMRAmazon GlueAmazon IAMAmazon KinesisAmazon RedshiftAmazon S3