Machine Learning Platform Engineer I

Mollie

Machine Learning Platform Engineer collaborating with teams to develop and maintain scalable ML solutions. Working with Python and Terraform on Mollie's cloud-based ML Platform based in Lisbon.

Posted 6/30/2026full-timeLisbon • 🇵🇹 PortugalJuniorWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in deploying and operationalizing Machine Learning models in production environments, with strong proficiency in Python and MLOps principles. Capable of enhancing cloud-based ML platforms and building CI/CD pipelines to ensure reliable workflows and model serving.

Highest-signal resume keywords

MLOps PrinciplesPython ProgrammingCI/CD Pipeline DevelopmentKubernetes ManagementGCP Familiarity

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

Machine Learning Model DeploymentExperiment TrackingModel VersioningData ValidationPythonScikit-learnPandasNumPyXGBoostLightGBM

Soft Skills

Attention to DetailContext-Switching Ability

Tools & Technologies

GCPDockerKubernetesKubeflowTerraformMLflow

Industry Keywords

MLOpsCloud-Based ML PlatformCI/CD PipelinesModel ServingOpen-Source AI Tooling

Tech Stack

Tools & technologies

CloudDockerGoogle Cloud PlatformKubernetesNumpyPandasPythonScikit-LearnSwitchingTerraform

About the role

Key responsibilities & impact

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.

Requirements

What you’ll 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

Benefits

Comp & perks

Health insurance
Professional development opportunities
Flexible working hours
Regular feedback and performance reviews