Machine Learning Platform Engineer I

Mollie; Deutschland; Vor Ort; EUR 85.000 - 120.000; Vollzeit

Zusammenfassung

Mollie’s Machine Learning Platform team is seeking a hands-on ML Platform Engineer to advance our cloud-native platform. Based at Mollie’s Lisbon Hub, you will write production-grade Python and Terraform, deploy models, and scale serving endpoints on Kubernetes while collaborating with ML Scientists and cross‑domain engineers.

You will own CI/CD for ML workflows, extend open-source tooling, and apply MLOps best practices to ensure reliability, observability, and reproducibility at scale.

Qualifikationen

• 1+ year of experience deploying and maintaining ML models in production.
• Strong understanding of MLOps principles including experiment tracking, reproducibility and monitoring.
• Proficient in Python with ML/data libraries (scikit-learn, pandas, NumPy).
• Familiar with cloud platforms, preferably GCP.
• Experience with CI/CD for ML workflows and container orchestration.

Aufgaben

• Collaborate with ML Platform Engineers and Scientists to deliver scalable ML solutions.
• Deploy and operationalize ML models to production and bridge experimentation to production.
• Enhance the cloud-based ML Platform on Kubernetes and Python/Terraform tooling.
• Build and maintain CI/CD pipelines for ML training and inference.
• Deploy, manage, and scale model serving endpoints on Kubernetes.
• Extend and host AI tooling and contribute to generative AI capabilities.
• Apply MLOps best practices for versioning, experiment tracking, data validation, and retraining.
• Ensure observability, monitoring, and alerting for infrastructure and models.
• Contribute to open-source tooling hosted at Mollie (e.g., LiteLLM, LibreChat).

Kenntnisse

Python programming

MLOps

Cloud platforms

Context switching

CI/CD for ML workflows

Tools

Docker

Kubernetes

Kubeflow

Terraform

MLflow

Jobbeschreibung

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 least 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