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Kraken, one of the world's longest-standing crypto platforms with over 10 million users, is seeking an AI Platform Engineer to design and operate its internal AI platform. The Enterprise Transformation team owns Kraken's AI infrastructure, which is deployed at scale across the organization with tiered spend controls, a governed integration gateway in production, and a system inventory being built against regulatory obligations.
You will own the technical layer of the AI platform end-to-end, from integration surface to production runbook. Key responsibilities include:
- Design and build the technical infrastructure of the AI platform, identifying capability gaps between what's possible and what the organization can safely use, then closing them through vendor evaluation or internal development.
- Own the governed integration gateway as an engineering surface, handling connector onboarding, authentication flows (OAuth, federated identity), reliability, and vendor escalation.
- Build internal tooling that extends the platform: 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, and running monthly reconciliation against vendor commitments.
- Implement technical controls for data handling, retention, and model eligibility policies, including identity integration, SSO, SCIM, entitlement by role and tier, and workspace configuration.
- Work directly with teams to unblock them by understanding workflows and building or configuring solutions that enable leverage from AI tools.
- Run technical enablement through deep dives for tool rollouts, office hours for model releases, and authority over engineering detail in knowledge base material.
- Administer the enterprise AI estate end-to-end: model turn-ups, feature enablement, deprecation, provisioning queue, vendor configurations, and seat true-ups.
This is a hands-on role requiring both software engineering capability and operational discipline. You'll work at the intersection of platform engineering, governance, and enablement, directly enabling the organization to safely and effectively use AI at scale.
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 comparable language should be natural
- Version control, CI, containers, and secrets management as everyday tools
- 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 equivalent IdP, OAuth, SCIM, SSO, and federated machine identity
- Operational discipline: intake queues, SLAs, runbooks, escalation hygiene, and instinct to document fixes
- Genuine teaching ability to explain technical capabilities to diverse audiences (compliance, traders, backend engineers)
- Clear written communication for technical documentation, runbooks, and policy pages
- Judgment about what to build; ability to distinguish between genuine organizational needs and demos that will be abandoned
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 (Jira, Confluence, JSM)
- Familiarity with regulated industry audit and evidence work in financial services
