DevOps and MLOps Engineer

Job description

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. The best scientists and engineers in the world are drawn to one thing: a problem worthy of their ability. At Qutwo, we have one. We are a European AI lab for the quantum era, co-founded by pioneers behind IQM and Silo AI, with a team spanning MIT, UC Berkeley, Cambridge, and Carnegie Mellon, and backed by the founders of Skype, Hugging Face, Quantinuum, Supercell and many more. We work at the intersection of quantum computing, AI, optimization, and simulation — where the answers do not yet exist and the work genuinely matters. We are seeking an exceptional DevOps and MLOps Engineer to build and operate the infrastructure that powers our quantum computing platform and AI solutions. If you thrive on bringing reliability and automation to fast-moving engineering teams, we encourage you to apply. Role Description As a DevOps and MLOps Engineer , you’ll work together with our team full-time primarily in Finland, Sweden, or Germany. You will 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. Qualifications we are looking for 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.