Level: Staff (MTS 4) · Reports to: Chief AI Officer, later the Senior Engineering Manager
The role. Build agents and the tools they use. Hands-on work on the reasoning layer, alongside the Agent Fleet Lead, with regular embeds beside the operators whose decisions you are encoding.
You will
Extend and maintain the tool surface across our infrastructure and business systems, which needs ongoing development and consolidation
Implement agents that diagnose fleet conditions and produce recommended actions with visible reasoning
Translate operator runbooks and tacit knowledge into agent behaviour, largely by sitting with the operators
Build evaluation fixtures from real historical incidents and measure agent performance against them
Rotate through Systems and SRE embeds
You have
Shipped production software with LLMs in the loop, including the unglamorous parts: retries, timeouts, malformed output, token and cost control
Strong Python
Experience building tool or function-calling interfaces that give models structured access to external systems
A demonstrable recent record of building and shipping rather than designing and reviewing
Curiosity about infrastructure and willingness to learn it from the people who run it
Nice to have: async Python at scale, retrieval pipelines, OpenTelemetry, prior work on evaluation harnesses
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