ML Platform Engineer: Scale Production ML on GCP

Mollie; Amsterdam; On-site; EUR 70,000 - 110,000; Full time

Job summary

Mollie is seeking a Machine Learning Platform Engineer to join the Machine Learning Platform team within the Data Domain. You will collaborate with ML Platform Engineers, ML Scientists, and domain engineers to deliver scalable ML solutions across Mollie.

You\'ll deploy models to production on GCP, develop CI/CD pipelines, manage Kubernetes endpoints, and contribute to tooling such as LiteLLM and LibreChat while advancing MLOps, observability, and automated retraining.

Qualifications

• 1+ year of experience deploying and maintaining ML models in production.
• Hands-on Python experience with ML libraries and data tooling.
• Familiarity with GCP and containerization (Docker).
• Plus: Kubernetes and Terraform for IaC.

Responsibilities

• Collaborate with ML Platform Engineers, ML Scientists, and engineers to deliver scalable ML solutions.
• Deploy and operationalize ML models to production in partnership with ML Scientists.
• Enhance and maintain Mollie\'s cloud-based ML Platform on GCP, writing production-grade Python and Terraform daily.
• Build and maintain CI/CD pipelines for ML training and inference workflows.
• Deploy, manage, and scale model serving endpoints on Kubernetes for low-latency inference.
• Contribute to hosting and extending open-source AI tooling (e.g., LiteLLM, LibreChat).
• Champion MLOps practices, including model versioning, experiment tracking, and data validation.
• Ensure platform reliability with observability, monitoring, and alerting for infrastructure and models.
• Support and expand generative AI capabilities within Mollie.

Skills

MLOps concepts

CI/CD for ML

Python programming

Tools

scikit-learn

pandas

NumPy

XGBoost

LightGBM

MLflow

Docker

Job description

Mollie is seeking a Machine Learning Platform Engineer to join the Machine Learning Platform team within the Data Domain. You will collaborate with ML Platform Engineers, ML Scientists, and domain engineers to deliver scalable ML solutions across Mollie.

You\'ll deploy models to production on GCP, develop CI/CD pipelines, manage Kubernetes endpoints, and contribute to tooling such as LiteLLM and LibreChat while advancing MLOps, observability, and automated retraining.