MLOps Engineer

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

MLOps Engineer - Amsterdam

Amsterdam, Netherlands · Permanent · Hybrid

What you'd actually work on

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

Where it gets technically interesting

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

What we're looking for

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

The company

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.

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