The opportunity
Elastic, the Search AI Company, enables everyone to find the answers they need in real time, using all their data, at scale — unleashing the potential of businesses and people. The Elastic Search AI Platform, used by more than 50% of the Fortune 500, brings together the…
What you'll do
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 loop.
Partner with the data science team on evaluation strategy: golden datasets, evaluators to gate on quality, latency and cost.
Raise the bar across the team: review designs and PRs, mentor engineers in eval-driven development, and write the technical proposals that shape the roadmap.
+ years of software engineering experience, with the recent years spent: shipping and operating AI-driven products on real production traffic, ideally products with public APIs and data models that had to evolve without breaking customers.
What they're looking for
- A track record of eval-driven product improvement: you have diagnosed agent or LLM behaviour from traces and user feedback, designed the evaluation that exposed the problem, shipped the fix and measured the outcome.
- Direct experience building agents with state and memory, and iterating on: prompts, skills and tool behaviour safely in production.
- Familiarity with MCP, including exposing public MCP servers and tools.
- Experience designing telemetry for AI systems, and using it to make engineering and product decisions.
