Principal Platform Engineer, Agentic AI Infrastructure (m/f/d)

You set up the agentic system that runs our analyses: the harness in which AI agents call our scientific tools under policy, the deterministic compute core they call, and the infrastructure that makes every run reproducible, metered, sealed and verifiable. The Quality Manager defines the standards; you implement them. You report to the CEO and work as a peer of our agentic engineering lead, with two platform engineers and the R developers who own the scientific tooling.

Key Responsibilities

• Make the scientific compute core deterministic: pinned R, Python and solver toolchains, content-addressed container images with a software bill of materials, and tests that prove byte-identical re-execution on a reference platform and tolerance-bounded re-execution across platforms.
• Build the harness: tool router, policy engine (allow-lists, parameter bounds, autonomy levels), sandboxed per-tenant executors without egress, usage metering per tool call, and the signed provenance bundle and certificate issuer behind every result.
• Expose the platform as a versioned API and as tools for AI agents with one shared contract, and ship the reproducibility package that re-executes a run outside the platform.
• Run the evaluation pipeline in continuous integration: conformance tests against reference case sets, re-execution tests, regression tests, results stored per procedure version.
• Own release engineering and operations: CI/CD pipelines, signed and content-addressed releases, scripted installation qualification, environment management (hosted, in a client tenant, later offline), monitoring, incident handling and the operating model the deployment engineer runs.
• Implement the technical controls behind the information security and AI management standards the Quality Manager owns.

• At least five years of platform, infrastructure or backend engineering with senior ownership of a production system.
• Reproducible builds and execution in practice: dependency pinning, artifact hashing, immutable versioning, supply chain integrity. Hermetic build systems are a strong plus.
• Strong Python; comfort around R and scientific computing toolchains. Containers and infrastructure as code. Experience with workflow or DAG runtimes and with tool-calling systems for AI agents.
• API design where the schema was a contract with external consumers, including versioning and deprecation in practice.
• Security engineering for multi-tenant systems: isolation, secrets, authorization, and awareness of the risks of language-model-driven systems. Exposure to GxP or equivalent regulated software is valuable; interest in acquiring it is required.
• DevOps practice at production grade: CI/CD, Kubernetes or an equivalent orchestrator, observability, release management for environments you do not fully control.

You will also benefit from:

• Flexible work hours and a home office policy that focuses on people and not on numbers
• An attractive remuneration package
• Regular in-person team gatherings at locations across Europe.
• A clearly defined technical problem with a commercial consequence, and the authority to solve it properly rather than quickly.
• A working environment in which your contribution will make a difference, and that allows you to take ownership of projects and processes
• A small, senior team that owns an open-source platform used by regulators and pharma, and the mandate to set the standard for certified scientific software.
• Responsibility, autonomy, participation, and career perspective

About the role