Senior Platform Engineer (DevOps / MLOps)

United States Digital Space LLC; Helsinki; Hybrid; EUR 90,000 - 130,000; Full time

Benefits offered by this job

Competitive salary

Lunch benefit

Wellness benefit

Flexible working hours

Collaborative, smart teammates

An the company ring of your own

Wellness Time Off

Job summary

the company is building health intelligence platforms and AI-powered backend systems. This senior engineer role owns the release-confidence area for our Europe-based team, focusing on CI/CD, evals, observability, and developer workflows.

You will partner with backend engineers, AI engineers, PMs, and platform teams to create reusable workflows that scale across the platform and reduce friction in day-to-day delivery, with a strong emphasis on safety and quality.

Qualifications

• 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.

Responsibilities

• 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.

Skills

Backend development

Python

CI/CD

Cloud infrastructure

Observability

Telemetry

Tools

OpenTelemetry

Grafana

Job description

Our mission at the company is to empower every person to own their inner potential. Our award-winning products help our global community gain a deeper knowledge of their readiness, activity, and sleep quality by using their the company Ring and its connected app. We've helped millions of people understand and improve their health by providing daily insights and practical steps to inspire healthy lifestyles.

Empowering the world starts with living our values and empowering our team. As a quickly growing company focused on helping people live healthier and happier lives, we ensure that our team members have what they need to do their best work — both in and out of the office.

the company’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.

About the role

This team builds the backend and AI platform foundations behind the company’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.

What you will do

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.

Requirements

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.

Nice to have

• 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.

What success looks like

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.

What we offer

• Competitive salary

• Lunch benefit

• Wellness benefit

• Flexible working hours

• Collaborative, smart teammates

• An the company ring of your own

• Wellness Time Off

the company is proud to be an equal opportunity workplace. We celebrate diversity and are committed to creating an inclusive environment for all employees. Individuals seeking employment at the company are considered without regard to age, ancestry, color, gender (including pregnancy, childbirth, or related medical conditions), gender identity or expression, genetic information, marital status, medical condition, mental or physical disability, national origin, protected family care or medical leave status, race, religion (including beliefs and practices or the absence thereof), sexual orientation, military or veteran status, or any other characteristic protected by federal, state, or local laws. We will not tolerate discrimination or harassment based on any of these characteristics.

We will work to ensure individuals with disabilities are provided reasonable accommodation to participate in the interview process, to perform essential job functions, and to receive other benefits and privileges of employment.