Requirements

Enterprise SaaS administration at 1,000+ seats, ideally including an AI or LLM platform
Demonstrated ability to build and ship working software, not just configure vendor products. Python or a comparable language should be natural to you
Experience integrating against LLM and SaaS APIs with working familiarity of agent and tool use patterns, including MCP or equivalent tool calling architectures
Working knowledge of identity and access. Okta or an equivalent IdP, OAuth, SCIM, SSO, and federated machine identity are things you've worked with
Operational discipline. You understand intake queues, SLAs, runbooks, escalation hygiene, and the instinct to document a fix the first time you make it
Genuine teaching ability. You can explain a technical capability three different ways on the same day to compliance, traders, and backend engineers
Clear written communication for technical documentation, runbooks, and policy pages
Judgment about what to build. You can tell the difference between a capability the organization actually needs and a demo that will be abandoned in a month

Nice to Haves

Experience standing up an internal developer or AI platform, including the self service and guardrail layers
Exposure to FinOps or software asset management practice
Experience with low code or workflow automation platforms
Atlassian administration depth in Jira, Confluence, or JSM
Familiarity with regulated industry audit and evidence work in financial services

Design and build the technical layer of the AI platform, from integration surface to production runbook
Own the governed integration gateway as an engineering surface
Build internal tooling that extends the platform
Configure and operate tiered spend controls across the AI estate
Implement the technical controls behind policy on data handling, retention, and model eligibility
Work directly with teams to unblock them
Run technical enablement
Administer the enterprise AI estate end to end