Sai Abhishree Pappusetty

About Sai Abhishree Pappusetty

AI/ML Engineer with 4+ years of experience Generative AI, LLMs, ML engineering, and cloud-native solution development across AWS, Azure, and enterprise environments. Proven track record designing production-grade NL→SQL platforms, automated RAG systems, and scalable AI workflows using Bedrock, AgentCore, LangChain, and transformer-based architectures. Skilled in building distributed ML pipelines, full-stack integrations, CI/CD automation, and secure identity systems with Azure AD, OAuth2/OIDC, and RBAC. Experienced in observability using OpenTelemetry, Langfuse, and GenAI monitoring tools, with strong proficiency across Python, JavaScript, SQL, TensorFlow, PyTorch, and modern MLOps. Adept at partnering with cross-functional teams to deliver high-impact AI solutions that enhance analytics, automation, and business decision-making.

Experience

1. Ai Engineer

   QuinStreet

   Oct 2025 — Present · Seattle, WA, US

   Designed and built Quinsight, an AWS-based NL→SQL analytics platform for QuinStreet, enabling business users to query Amazon Redshift data in natural language using AWS Bedrock (Claude 3.5) in a production-grade environment- Implemented a request router and identity-aware inline agents with Bedrock AgentCore and AWS Strands to route traffic between FAQ fast-path and NL→SQL workflows, enforcing tenant/role-based policies and least-privilege tool access- Built the FAQ fast-path service on DynamoDB with parameterized SQL templates and Redshift Data API integration, delivering low-latency responses for recurring questions and reducing load on the NL→SQL pipeline- Configured enterprise-grade authentication and authorization for Quinsight using Azure Active Directory, implementing OAuth2/OIDC flows, role-based access control (RBAC), and secure token validation layers to ensure protected access for internal and partner applications- Integrated full-stack observability using OpenTelemetry, OpenLLMetry, Langfuse, and GenAI observability tooling, enabling trace-level visibility across NL→SQL agents, Lambda workflows, Bedrock calls, and Redshift queries; improved debugging efficiency and reduced MTTR for production issues- Contributed to infrastructure-as-code and deployment automation with AWS CDK/Terraform for API Gateway, Lambda, S3, DynamoDB, and Redshift integrations, ensuring reproducible environments across dev/stage/prod- Partnered with frontend teams to expose a single automation-friendly router API, where React calls one endpoint and the backend orchestrates all downstream agents, tools, and visualisation workflows.

Education

• The George Washington University

  Master of Science - MS, Computer Science/ Data Science

• SRM University, AP

  Bachelor of Technology - BTech, Computer Science

  2018 — 2022