What You'll Own

• Design and build deployment infrastructure for web and data services.
• Build scalable MLOps infrastructure to support model training, validation and serving.
• Implement and maintain CI/CD pipelines for application and ML deployments.
• Collaborate with data scientists and software engineers to streamline development workflows and solve production issues.
• Drive best practices for observability, monitoring, incident response and cost optimisation.
• Document deployment patterns, runbooks and operational playbooks for the team.

Required skills & experience

• 4+ years minimum, 6+ years preferred in DevOps, MLOps or related platform engineering roles.
• Hands-on experience with orchestration tooling (Nomad, Kubernetes, or similar).
• Proficiency with Infrastructure as Code — Terraform strongly preferred.
• Solid familiarity with CI/CD tooling and pipelines supporting both application and ML workflows.
• Practical knowledge of ML pipelines, model versioning and serving patterns.
• Strong communicator who can collaborate with data scientists and software engineers.
• Ability to work overlapping CET (±3 hours) or US East Coast timezones.

Tech stack (what we use / what helps)

• Python, AWS & GCP, TypeScript, React, SQL.
• Infrastructure: Terraform, Docker, (Kubernetes / Nomad), CI/CD systems.
• ML tooling: model training orchestration, model serving frameworks, experiment tracking.
• You don’t need to know every technology — we value engineers who are language-agnostic and quick to learn.