Case study / AI product
AI Product Development at Glean
End-to-end delivery of enterprise AI features for knowledge search, AI assistants, and RAG workflows across the full stack.
Overview
As a Senior AI Product Developer at Glean, I own end-to-end delivery of AI-enabled SaaS features, spanning React/TypeScript frontend modules, Python/Node.js backend services, APIs, data workflows, and LLM integrations.
What I build
- Enterprise AI experiences for knowledge search, AI assistants, semantic retrieval, workflow automation, summarization, and content generation.
- Backend services that connect application workflows with enterprise data sources, permission-aware search, embeddings, vector retrieval, and LLM-powered reasoning flows.
- RAG-style workflows that improve AI response relevance, grounding, and usability across document-heavy business use cases.
- Engineering foundations: architecture decisions, technical documentation, reusable components, and shared standards for how the team builds AI products.
Technologies
- Frontend: React, TypeScript
- Backend: Python, Node.js, REST APIs
- AI: OpenAI API, LLM integration, RAG, embeddings, vector search
Collaboration & impact
I work with product, design, ML, QA, and platform teams to turn AI ideas into production features with clear UX, reliable APIs, and product value we can measure. Once they ship, I keep them fast and dependable by tuning API flows, cleaning up data access patterns, debugging production issues, and adding test coverage.
Glean is an enterprise AI platform. The work described here reflects my contributions as a member of the engineering team.