Senior Platform Engineer (DevOps / MLOps)

Oura’s engineering organization consists of talented developers distributed across the EU and US. For day-to-day feature work, our engineers are organized into smaller cross-functional teams. Our teams have a great deal of autonomy and are responsible for the design, development and architecture of their features. Teams take full ownership of their code and handle everything from concepting, design and implementation to release, maintenance and bug fixes. 

This team builds the backend and AI platform foundations behind Oura’s health intelligence experiences. The role focuses on making AI-powered backend work safe to ship repeatedly, easy to validate before release, and easy to debug in production.

The opportunity in this role is to strengthen the production harness around our platform while also making day-to-day development more convenient for the engineers building on it. That includes dependable quality gates, reusable validation flows, strong observability, and developer-facing workflows that reduce friction instead of adding more process.

We are looking for a senior engineer to own this area for our Europe-based team. This role sits at the intersection of developer workflows, CI/CD, AI evals, and operational feedback loops. You will build the systems that help feature teams understand whether a change is ready to ship and improve it before issues become member-facing problems.

This is a platform-engineering role focused on release confidence and developer convenience for AI-powered backend systems.

You do not need to do all of these on day one, but these are the kinds of problems you’ll own:

• →Build the CI&D quality gates for backend and AI workflows so engineers get faster, more trustworthy signals before changes reach production.
• →Create reusable validation infrastructure for AI-powered backend features, including scenario-based evals, staging validation flows, generated test profiles, and higher-signal E2E checks.
• →Improve the feedback loop between development and production by connecting eval results, CI outcomes, runtime telemetry, and member-facing signals into one operational picture.
• →Help standardize the shared LLM delivery surface in practice: unified client patterns, trace capture, environment setup, and operational guardrails that feature squads can adopt without bespoke platform work.
• →Partner closely with backend engineers, AI engineers, PMs, and adjacent platform teams to turn release-confidence needs into reusable workflows that scale across the platform rather than one-off fixes for a single squad.

We’d love to hear from you if you have:

• Strong hands-on experience building and operating production backend platforms, developer infrastructure, CI/CD systems, or internal engineering tooling used by real product teams.
• Strong software engineering depth in a backend language such as Python, plus practical experience with cloud infrastructure, production debugging, and maintainable system design.
• Experience designing validation or quality systems that teams actually trust: CI gates, eval flows, test infrastructure, telemetry pipelines, or release automation.
• A track record of improving delivery confidence for other engineers, not just operating infrastructure manually.
• Comfort supporting modern AI or LLM-backed systems in production through some combination of evals, tracing, rollout safety, review signals, or operational feedback loops.
• Strong collaboration and communication skills, especially when translating vague platform pain points into practical systems that many teams can use.

• Experience with offline and online eval systems, LLM tracing, or scenario-driven testing.
• Experience improving CI/CD, developer workflows, or review pipelines used across many repositories or services.
• Familiarity with observability and telemetry tooling such as OpenTelemetry, Grafana, or similar ecosystems.
• Background in health-tech, wearables, or other quality-sensitive domains where operational rigor matters.

In this role, success means the team has a clear, repeatable release-confidence system for AI-powered backend changes. Feature teams know which checks matter, can run them early, and trust the results enough to use them in day-to-day delivery.

A strong outcome in the first 6–12 months would be a reusable validation stack for AI features: scenario-driven evals, higher-signal E2E checks, better staging confidence, and traceability from production issues back to the model, prompt, tool, or configuration change that caused them. This should make it easier for product teams to ship AI-powered capabilities with confidence, while giving the platform team a much tighter feedback loop on quality, reliability, latency, and cost.