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.
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.
You will need:
- 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