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Machine Learning Platform Engineer I

Mollie · Lisbon

RemoteEntry levelData and MLEngineeringMachine LearningPosted 4 weeks ago
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About this role

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

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