Principal Platform Engineer, DevOps and Deployment (Regulated AI Software)

Role overview

Own the platform foundations that let scientists run analyses reproducibly, deliver them into client environments and verify results later. You’ll lead the compute core, release pipeline, deployment operations, usage metering and result provenance for regulated scientific software. The AI layer has a separate lead; you’ll build its runtime and coordinate on shared services and controls.

Responsibilities - Make scientific execution deterministic by pinning R, Python and solver dependencies, producing content-addressed container images with software bills of materials, and validating repeatability across reference and varied platforms. - Build isolated, network-restricted execution services for each client, including usage metering and signed records or certificates for results. - Develop the policy engine and tool router with the AI lead, and provide a versioned API and agent tools under a shared contract. - Package runs so they can be reproduced outside the platform, and maintain continuous integration checks for conformance, reruns and regressions by procedure version. - Own release engineering and operations, including signed releases, installation qualification, hosted and client-tenant environments, monitoring and incident response. - Implement technical safeguards that support information security and AI management standards.

Requirements - At least five years in platform, infrastructure or backend engineering, with senior ownership of a production system. - Practical experience with reproducible builds and execution, dependency pinning, artifact hashing, immutable versions and software supply-chain integrity. - Experience with Kubernetes or a comparable orchestrator, monitoring and releases into environments outside your full control. - API design experience where schemas serve external consumers, including versioning and deprecation. - Security engineering for multi-tenant systems, covering isolation, secrets and authorization, plus awareness of risks in language-model-driven systems. - Interest in learning regulated software practices such as GxP, or equivalent experience.

Nice to have - Experience with hermetic build systems.

Benefits and work setup - Flexible hours and a people-focused home office policy. - Regular in-person team gatherings across Europe. - Attractive remuneration, autonomy and ownership in a small, senior, cross-disciplinary team.