MLOps Engineer

The French Sourcer; Amsterdam; On-site; EUR 90,000 - 130,000; Full time

Benefits offered by this job

Health insurance

Pension plan

Equity plan

Flexible working

Job summary

The French Sourcer is seeking an MLOps Engineer to join their Amsterdam team in a hybrid role. You will build and maintain scalable production systems for AI models, focusing on training, deployment, monitoring, and reproducibility across cloud and Kubernetes environments.

You will develop automated pipelines for data prep, training, validation, and release, while implementing model versioning, drift monitoring, and retraining workflows.

Qualifications

• 4+ years of experience in MLOps, machine learning engineering, platform engineering, or a related role.
• Strong Python skills and production ML deployment experience.
• 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.

Responsibilities

• Build and maintain infrastructure for model training, deployment, and monitoring.
• Develop automated pipelines for data preparation, training, validation, and release.
• Deploy ML models through batch and real-time inference services.
• Create reproducible environments for experiments and production workloads.
• Implement model versioning, approval, rollback, and retraining processes.
• Monitor model performance, feature quality, drift, latency, and infra usage.
• Collaborate with ML engineers to move models into production.
• Collaborate with data engineers to improve training and inference data reliability.
• Manage containerised workloads across cloud and Kubernetes environments.
• Improve CI/CD processes for ML services.
• Control compute usage and infrastructure costs.
• Document production dependencies, ownership, and recovery procedures.

Skills

Python

Docker

Kubernetes

AWS

Azure

GCP

MLflow

Kubeflow

SageMaker

Vertex AI

CI/CD

Terraform

English

Job description

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.