Machine Learning Platform Engineer I

Your Opportunity

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.

This is a hands-on role where you will spend the majority of your time writing Python and Terraform alongside a team of skilled ML Platform Engineers. Based at Mollie's Lisbon Hub, you will be part of a geographically distributed team spanning Amsterdam and Lisbon, working in a collaborative environment that embraces both remote and hybrid ways of working.

What you’ll be doing

As an ML Platform Engineer, you will:

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, bridging the gap between experimentation and real-world impact

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, ensuring reliable and automated workflows across environments

Deploy, manage, and scale model serving endpoints on Kubernetes, ensuring low-latency, high-availability inference for production workloads

Assist in extending, developing, and hosting custom and open-source AI tooling ; enabling teams to rapidly build and deploy AI-powered solutions.

Champion MLOps best practices, implementing standards around model versioning, experiment tracking, data validation, and automated retraining

Ensure platform reliability by setting up observability, monitoring, and alerting for both infrastructure and deployed models

Maintain and enhance open-source AI tooling hosted at Mollie (such as LiteLLM and LibreChat), and further support and expand our generative AI capabilities.

What you'll bring

1+ year of experience deploying and maintaining ML models in production

Good understanding of MLOps principles, including matters such as 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 at 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, adapting quickly to shifting priorities

Preferably familiarity with infrastructure-as-code (IaC) tools such as Terraform

Experience building and maintaining CI/CD pipelines for ML workflows

Grow your way