Principal AI Engineer (Context, Agents and Context)

Description du poste

The Context Engine team builds the knowledge layer that AI agents use to work with enterprise data in Elasticsearch. We extract knowledge from any data sources into a structured AI Index, serve it to agents through public APIs, MCP tools and framework integrations, and close the loop with agent traces so that what the engine knows improves from real usage. Any agent can use it: Elastic’s own Agent Builder, Claude Code, LangChain and other third-party harnesses As a Principal AI Engineer, you own the improvement loop of this product end to end: how agents, automations and skills behave in production, how we observe them, how we evaluate them, and how we ship changes to them safely This is a hybrid role at the intersection of engineering, data science, and product. You will write production code, design evaluation and telemetry that product decisions can rest on, and set the technical bar for how the team iterates on agentic behaviour You will work alongside data scientists, backend engineers, product, and UX, and your work will show up directly in what customers build on top of Elastic The codebase is TypeScript and we build it in the open, so you’ll be shipping code, designs and discussions in public alongside the rest of the Elastic Stack Own the production improvement loop for Context Engine: understand how extraction automations, retrieval tools and memory behave, based on offline evaluations and customer conversations and telemetry. You help find the failure modes, fix them, and prove the fix Define how we iterate on agents and skills safely: versioning and rollout of prompts, skills and automations, regression coverage, staged and shadow evaluation, and the guardrails that let us change behaviour without breaking customers Design the telemetry we need to make data-informed engineering decisions: what to capture from agent traces, tool calls and knowledge retrieval, how it lands in Elasticsearch, and how it feeds evaluation, dashboards and the feedback lo

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