Experience standing up an internal AI or developer platform function rather than inheriting a mature one. You have written the charter, defined intake, and built the roadmap from a blank page
The ability to hold scope. You can take a commitment that was made without scoping or resourcing, land a bounded first version, and decline the rest without it reading as abdication
Fluency in AI cost engineering: usage attribution, model right-sizing, token economics, agent cost traceability, and the difference between a hard limit and a useful guardrail
An enablement-first instinct. Your default question is how to make something safe to self-serve. When you do have to decline access, you always explain why and offer an alternative path to the same visibility
Hands-on technical depth across LLM platform tooling, agent and MCP architecture, identity and OAuth, CI/CD, and infrastructure as code. Enough to review a design honestly and tell the difference between real risk and reported progress
Comfort building for an audience that is no longer only engineers. A growing share of the people shipping internal tools are not developers, and the platform has to work for them without lowering the security bar
Data governance fluency in a regulated environment. You know what should never enter a prompt, a repository, or a third-party tool, and you can make that judgment fast without becoming the bottleneck
Comfort operating through influence. Product AI, security policy, data platform, and budget authority all sit in partner organizations
8+ years in platform, infrastructure, or systems engineering, including 3+ years managing and developing engineers, ideally at a company that scaled through a significant headcount inflection
Must be authorized to work in the U.S. without the need for current or future employer sponsorship
Open to using AI to amplify their skills and strengthen their work, demonstrating curiosity, a willingness to learn, and sound judgment in applying AI responsibly to improve efficiency and impact