Uber · KA, India
Skills: Python, Java
About the role and team
At Uber, AI agents are increasingly writing, reviewing, testing, and debugging code in production. They deliver high-quality results efficiently, without wasting tokens, only when they understand Uber's codebases. The AI Context Engineering team builds the context foundation that every software-development AI agent at Uber relies on:
AI Context Graph: a unified graph of services, ownership, domain entities, and business flows Code Graph: a graph representation of Uber's monorepos built for agents to consume AI-Generated Documentation: documentation that stays fresh as code changes Agentic Memory: a memory layer that lets agents learn across sessions
As a Senior Software Engineer, you will own major parts of this platform end to end. You'll take them from prototype to production at monorepo scale, and work with partner teams to integrate foundational context products with the agents running in production for workflows such as root-cause analysis, code authoring, test planning, and library upgrades, etc
What will you do
Context Graph and Ontology: Enrich the graph with new entity types, such as data streams, metrics, and observability signals, and link them to code so agents can reason across systems. Code Graph Infrastructure: Build and scale code graphs across multiple monorepos and microrepos, covering symbols, call edges, dependencies, and links between code and tests. Add incremental updates, versioned (historical) graphs, and function-level embeddings, and serve the graphs to agents with low latency through local and remote interfaces. AI-Generated Documentation: Extend automated documentation from backend services to mobile (screens, UI components), libraries, and data pipelines. Use refresh strategies that keep context current as code changes. Agentic Memory: Design the memory layer that lets agents persist and reuse decisions, past fixes, and team preferences across sessions. Standards and Governance: D …truncated… latency, or CI time. Agen…
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