MLOps Engineer

Our client is scaling their AI/ML platform and needs a hands‑on MLOps Engineer to own the infrastructure, automation, and reliability of their machine‑learning lifecycle. This is a role for someone who thrives in high‑impact environments where models must move to production with speed, safety, and repeatability.

You’ll work with data scientists and platform engineers to build and maintain the tooling, pipelines, and cloud infrastructure that power production ML systems.

Fully remote, open to candidates based anywhere in Europe.

Key Responsibilities

• Build and maintain end‑to‑end ML pipelines for training, deployment, and monitoring
• Develop CI/CD workflows for ML and data pipelines
• Implement model observability (drift, performance, alerting)
• Productionise models in collaboration with data science teams
• Manage cloud‑native ML infrastructure (AWS/GCP/Azure)
• Drive best practices in reproducibility, governance, and automation

Required Experience

• Strong experience with MLOps tooling (MLflow, Kubeflow, SageMaker, Vertex AI)
• Solid Python skills and familiarity with ML frameworks
• Kubernetes + Docker proficiency
• CI/CD experience (GitHub Actions, GitLab CI, Argo, Jenkins)
• Cloud engineering background
• Infrastructure‑as‑code (Terraform preferred)