Senior AI Platform Engineer

Job Mission

As a Senior AI Platform Engineer, you’ll join the Agentic Enablement side of our Platform team and help build the technical foundation for how Tibber adopts and scales agentic systems across engineering.

Your focus will be on the architecture, workflows and trust infrastructure that allow AI agents to safely support software development - from writing and testing code to reviewing changes, triggering workflows and knowing when to involve a human.

This is a platform role, which means the engineers building agents at Tibber are your users. Your success is measured by how quickly and confidently they can move from idea to production without needing you in the room.

This role is for someone who enjoys building reliable systems, thinking deeply about architecture and helping teams adopt new ways of working with confidence.

• Contribute to the design and architecture of Tibber’s agentic development platform, including orchestration layers, platform integrations, feedback loops and escalation paths, and the developer-facing CLI and onboarding experience.
• Take ownership of key platform components, from design and implementation to reliability and continuous improvement.
• Help define workflows, quality standards and ownership models for how agents can write, test, review and support code.
• Help shape safe and reliable agentic workflows, including human-in-the-loop boundaries.
• Build trust infrastructure that makes agentic systems observable, secure, reversible and reliable in production.
• Work closely with Staff Engineers and the wider Platform team to build on Tibber’s AWS, Kubernetes and infrastructure foundations.
• Bring people along through clear technical communication, thoughtful collaboration and strong engineering judgment.

You are driven by impact and know how to turn strategy into execution. You're used to fast-paced environments and thrive when given clear goals and high accountability.

• Bring hands‑on production experience building and running reliable agentic software systems.
• Treat evaluation as a first‑class engineering concern, you’ve built eval pipelines for non‑deterministic systems, whether LLM‑as‑judge, regression suites, or golden datasets, and know that measuring agent quality in production is an ongoing discipline, not a launch checklist.
• Have strong infrastructure knowledge across Kubernetes, Terraform, Infrastructure as Code, and platform networking.
• Demonstrate strong software engineering depth in Type Script, with the ability to own and operate services end‑to‑end.
• Enjoy communicating technical directions and collaborating with engineering teams to enable adoption, and turn platform capabilities into real impact.
• Apply a strong security mindset to agent permissions, least‑privilege designs, and blast radius awareness.
• Have working knowledge of Model Context Protocol (MCP), evaluation frameworks, or cloud capabilities like AWS Bedrock.