This list contains only the countries for which job offers have been published in the selected language (e.g., in the French version, only job offers written in French are displayed, and in the English version, only those in English).
We are looking for a Machine Learning Platform Engineer to join Mollie's Machine Learning Platform team, sitting within our broader Data Domain. Our ML Platform empowers Machine Learning Scientists to develop and deploy custom ML solutions at scale across Mollie, serving domains including Risk & Fraud, Payments, Merchant Experience, Financial Services, Go-to-Market, and more. As the central team responsible for Mollie's Machine Learning Platform, we own the maintenance and continuous enhancement of the platform, ensuring it remains reliable, scalable, and fit for production-grade workloads. We work closely with domain teams to bring custom ML models into products, bridging the gap between research and real-world impact, while also designing and developing custom GenAI tooling and platforms for both internal employees and Mollie's customers.
Job Responsibility
Collaborate closely with ML Platform Engineers, Machine Learning Scientists, and engineers across Mollie's domain teams to deliver scalable Machine Learning solutions
Deploy and operationalize ML models to production in partnership with Machine Learning Scientists
Enhance and maintain our cloud-based ML Platform on GCP, writing production-grade Python and Terraform daily
Build and maintain CI/CD pipelines for ML model training and inference
Deploy, manage, and scale model serving endpoints on Kubernetes
Assist in extending, developing, and hosting custom and open-source AI tooling
Champion MLOps best practices
Ensure platform reliability by setting up observability, monitoring, and alerting
Maintain and enhance open-source AI tooling hosted at Mollie
Requirements
1+ year of experience deploying and maintaining ML models in production
Good understanding of MLOps principles, including experiment tracking, reproducibility, pipeline automation, model versioning, and monitoring in production
Strong hands-on Python programming skills, with proficiency across common ML and data libraries such as scikit-learn, pandas, NumPy, XGBoost, LightGBM, and MLflow
Familiarity with a major cloud platform, preferably GCP
Experience with containerization (Docker), with preferred familiarity in container orchestration tools such as Kubernetes and Kubeflow
Strong context-switching ability with sharp attention to detail
Preferably familiarity with infrastructure-as-code (IaC) tools such as Terraform
Experience building and maintaining CI/CD pipelines for ML workflows