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 SageMaker 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.

A search run by The French Sourcer recruitment built for technical teams.