Description
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.
You will collaborate closely with ML Platform Engineers, Machine Learning Scientists, and engineers across Mollie's domain teams to deliver scalable Machine Learning solutions.
Key responsibilities include:
- 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
- Strong hands-on Python programming skills
- Familiarity with a major cloud platform, preferably GCP
- Experience with containerization (Docker) and container orchestration tools such as Kubernetes
- Strong context-switching ability with sharp attention to detail
- Preferably familiarity with infrastructure-as-code (IaC) tools such as Terraform
At Mollie, growth is personal. We believe everyone should have the chance to develop their skills, explore new challenges and shape their career on their own terms.