MLOps Engineer

Build the production systems that allow AI models to move beyond experimentation and operate reliably at scale.

Amsterdam, Netherlands
· Permanent
· Hybrid

• Building and maintaining infrastructure for model training, deployment, and monitoring
• Developing automated pipelines for data preparation, training, validation, and release
• Deploying machine learning models through batch and real-time inference services
• Creating reproducible environments for experiments and production workloads
• Implementing model versioning, approval, rollback, and retraining processes
• Monitoring model performance, feature quality, drift, latency, and infrastructure usage
• Working with machine learning engineers to move models into production
• Working with data engineers to improve the reliability of training and inference data
• Managing containerised workloads across cloud and Kubernetes environments
• Improving CI/CD processes for machine learning services
• Controlling compute usage and infrastructure costs
• Documenting production dependencies, ownership, and recovery procedures

• Maintaining consistency between training and production environments
• Supporting both scheduled batch predictions and low-latency online inference
• Automating retraining without deploying models that have not passed the required checks
• Detecting changes in feature distributions before model performance declines
• Managing GPU and CPU workloads with different performance and cost requirements
• Reproducing a specific model version with the correct code, parameters, and training data
• Rolling out and rolling back models without interrupting production services

• 4+ years of experience in MLOps, machine learning engineering, platform engineering, or a related role
• Strong Python skills
• Experience deploying machine learning models in production
• Practical knowledge of Docker and Kubernetes
• Experience with cloud platforms such as AWS, Azure, or GCP
• Familiarity with MLflow, Kubeflow, Sage Maker, Vertex AI, or comparable tooling
• Experience building CI/CD pipelines and automated workflows
• Understanding of model monitoring, versioning, retraining, and drift
• Knowledge of infrastructure as code, preferably Terraform
• Ability to work across machine learning, data, and infrastructure layers
• Professional English

A European technology company developing AI-enabled products for business customers. Its machine learning teams are moving from individual production use cases towards a shared platform and consistent engineering standards.

Health insurance, pension contribution, equity plan, and flexible working.

Languages:

Professional English.