AI Platform Engineer, Enablement and Governance Operations

Kraken

3d•$110,400 - $220,800

About The Position

Enterprise Transformation owns Kraken's internal AI platform. AI tooling is deployed at scale across the org. Tiered spend controls are live, a governed integration gateway is in production, and a system inventory is being built against real regulatory obligations. The next constraint is not access. It's capability. The frontier moves every few weeks, and most of what arrives is a protocol, API, or primitive that someone has to turn into something the rest of the company can safely use. This role will design and build the technical layer of the AI platform, from integration surface to production runbook. It involves identifying capability gaps between what's possible and what the organization can safely use, then closing them by evaluating vendors or building internally. The role also includes owning the governed integration gateway, building internal tooling, configuring and operating tiered spend controls, implementing technical controls behind policy on data handling, retention, and model eligibility, working directly with teams to unblock them, running technical enablement, and administering the enterprise AI estate end to end.

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.
Proficiency in Python or a comparable language.
Familiarity with version control, CI, containers, and secrets management.
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, including Okta or an equivalent IdP, OAuth, SCIM, SSO, and federated machine identity.
Operational discipline, understanding intake queues, SLAs, runbooks, escalation hygiene, and the instinct to document fixes.
Genuine teaching ability to explain technical capabilities to diverse audiences.
Clear written communication for technical documentation, runbooks, and policy pages.
Judgment about what to build, differentiating between organizational needs and transient demos.

Nice To Haves

Experience standing up an internal developer or AI platform, including 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.

Responsibilities

Design and build the technical layer of the AI platform, from integration surface to production runbook.
Identify capability gaps between what's possible and what the organization can safely use, then close them by evaluating vendors or building internally.
Own the governed integration gateway as an engineering surface, handling connector onboarding, authentication flows including OAuth and federated identity, reliability, and vendor escalation when things break.
Build internal tooling that extends the platform, such as agent scaffolding, reusable skills and prompt assets, evaluation harnesses for model changes, provisioning automation, and integrations between AI tooling and systems of record.
Configure and operate tiered spend controls across the AI estate, setting caps by role and tier, building alerting rules, managing exception queues, running monthly reconciliation against vendor commitments, and eliminating single author risk in cap automation.
Implement the technical controls behind policy on data handling, retention, and model eligibility, including identity integration, SSO and SCIM, entitlement by role and tier, and workspace configuration where retention terms differ by model class.
Work directly with teams to unblock them by understanding their workflow and building or configuring the necessary tools.
Run technical enablement, including deep dives for tool rollouts, office hours for model releases, and providing engineering detail for knowledge base material.
Administer the enterprise AI estate end to end, including model turn ups, feature enablement, deprecation, provisioning queue, vendor configurations, and seat true ups.