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Mollie is seeking a Machine Learning Platform Engineer to join its ML Platform team within the Data Domain. The ML Platform empowers Machine Learning Scientists to develop and deploy custom ML solutions at scale across Mollie's business domains including Risk & Fraud, Payments, Merchant Experience, Financial Services, and Go-to-Market.
In this hands-on role, you will spend the majority of your time writing Python and Terraform as part of a team of skilled ML Platform Engineers. You'll be based at Mollie's Lisbon Hub while working with a geographically distributed team spanning Amsterdam and Lisbon in a hybrid environment.
Key responsibilities include:
- Collaborating with ML Platform Engineers, Machine Learning Scientists, and domain teams to deliver scalable ML solutions
- Deploying and operationalizing ML models to production, bridging experimentation and real-world impact
- Enhancing and maintaining the cloud-based ML Platform on GCP with production-grade Python and Terraform
- Building and maintaining CI/CD pipelines for ML model training and inference
- Deploying, managing, and scaling model serving endpoints on Kubernetes for low-latency, high-availability inference
- Extending, developing, and hosting custom and open-source AI tooling to enable rapid AI solution deployment
- Championing MLOps best practices including model versioning, experiment tracking, data validation, and automated retraining
- Setting up observability, monitoring, and alerting for infrastructure and deployed models
- Maintaining and enhancing open-source AI tooling hosted at Mollie (such as LiteLLM and LibreChat)
Mollie emphasizes personal growth, with regular feedback, performance reviews, and transparent salary reviews. The company actively promotes internal mobility and supports employees exploring new roles, projects, and career paths.
REQUIREMENTS:
- 1+ year of experience deploying and maintaining ML models in production
- Good understanding of MLOps principles (experiment tracking, reproducibility, pipeline automation, model versioning, monitoring)
- Strong hands-on Python programming skills with proficiency in ML and data libraries (scikit-learn, pandas, NumPy, XGBoost, LightGBM, MLflow)
- Familiarity with a major cloud platform, preferably GCP
- Experience with containerization (Docker) and container orchestration tools (Kubernetes, Kubeflow preferred)
- Strong context-switching ability with sharp attention to detail
- Preferably familiar with infrastructure-as-code tools such as Terraform
- Experience building and maintaining CI/CD pipelines for ML workflows
