Level

Mollie

Machine Learning Platform Engineer I

Mollie is hiring a Machine Learning Platform Engineer I for a remote role open to applicants in Netherlands. Level rates it ; you can apply on Level.

AI in this role

scikit-learnxgboostmlflow
ml-ops

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 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

Grow your way

At Mollie, growth is personal. We believe everyone should have the chance to develop their skills, explore new challenges and shape their career on their own terms.

You'll get regular feedback and performance reviews to support your development, with fair and transparent salary reviews along the way. Many Mollies move into new roles or take on new projects to stretch themselves, and we actively hire from within to help you take the next step.

No matter if you're aiming for promotion, exploring a different career path or building new skills, you'll be supported with the tools, trust and opportunities to grow your way.

Unlock your full potential and join us to eliminate financial bureaucracy. If you're excited by the idea of building what's next, for yourself and for thousands of businesses across Europe, we'd love to hear from you.

 

AI at Mollie

We believe in Always Be Shipping, and AI brings that philosophy to life across every team, every role, every day.

AI is core to how we build. It helps us move faster, simplify work and make smarter decisions, creating real impact for the businesses we serve. We're looking for people who are excited to use AI to shape the future of finance with us.

How we rate this

Machine Learning Platform Engineer I at Mollie rates 96 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

ML Opsscikit-learnXGBoostMlflow

Questions you could be asked

  1. How do you monitor a model once it's live, and how do you know it needs retraining?
  2. Walk me through how you've used scikit-learn in your day-to-day work.
  3. What are the limits of XGBoost that you've run into, and how did you work around them?
  4. What's a project where you used Mlflow hands-on?
  5. How would you decide a model or AI system is ready to ship?

Adapt your resume

  • List these exact terms on your resume: ML Ops, scikit-learn, XGBoost, and Mlflow. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
  • Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.

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