DevOps and MLOps Engineer
At a glance
Mid-level ML Platform & Ops role at Qutwo. Helsinki.
Pay not stated ML Platform & Ops salary
aimlroles summary, based on the employer's posting.
What you'll do
- Build and maintain CI/CD pipelines, cloud infrastructure and ML workflows that support reliable, continuous releases
- Work with engineering and research teams on platform development and AI projects for clients
- Support production infrastructure and ML operations across the software delivery process
What you bring
- At least five years of production work in DevOps, platform engineering or MLOps
- Strong Kubernetes and Docker skills, including debugging networking issues and managing node evictions
- You can build modular infrastructure as code and dependable pipelines for multiple environments
- You use Python and Bash to automate complex work and create internal tools
- You have production experience with ML pipelines or model deployment, or are a senior engineer ready to learn MLOps
Who this fits
This role suits a hands-on engineer who has operated infrastructure in production and can work through changing needs. The work spans platform development with engineering and research teams, as well as client-facing AI projects.
From the employer
Join Qutwo - Europe's private Quantum AI Lab, on our quest to bridge the gap between the classical and the quantum world. We partner with the most advanced and forward-thinking customers, solving fundamental real-world problems, side by side.
We are seeking an exceptional DevOps and MLOps Engineer to build and maintain CI/CD pipelines, cloud infrastructure, and ML operations workflows that enable Qutwo to ship reliably and continuously. You will work across engineering and research teams, supporting both platform development and client-facing AI projects.
Requirements
5+ years of actual production experience in DevOps, Platform Engineering, or MLOps. You’ve owned infrastructure at scale and have the scar tissue to prove it.
Deep Kubernetes & Docker knowledge. You know how to debug K8s networking, handle node evictions, and optimize containers for production, not just local dev.
Production-grade IaC & CI/CD. You write clean, modular Terraform (or similar) and design reliable, multi-environment pipelines that don't constantly break.
Strong Python & Bash scripting. You write clean, maintainable code to automate complex tasks and build internal tooling.
Real observability experience. You’ve used Prometheus, Grafana, and OpenTelemetry to actually diagnose and solve production outages, not just stare at dashboards.
ML Infrastructure. You either have experience with ML pipelines and model deployment, or you're a senior engineer who is genuinely excited to dive deep into MLOps.
Comfortable with ambiguity. We move fast, and our infrastructure needs to evolve with the business. You’re someone who figures things out and helps the team grow.
Experience in one or more of the following areas is a plus but not a requirement:
Quantum computing or scientific computing environments.
High-performance computing environments.
Enterprise SaaS platform development.
GPU workload orchestration.