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.We are excited to invite applications for the position of ML Engineer to join our growing team of Data Scientists (DSs) and Machine Learning Engineers (MLEs). Our team is a central entity that develops and deploys machine learning capabilities across Mollie, contributing to various Domains, including Monitoring (Risk & Fraud), Payments, Merchant Experience, Financial Services, Go-to-Market, and more. We have built and maintain a multi-cloud ML Platform for model development and production, as well as MollieGPT, our in-house GenAI solution for both internal employees and our customers. This role is hands-on, where you will spend most of your time coding in Python and Terraform alongside other ML Engineers and Data Scientists. The position is based at Mollie's Lisbon Hub, and you will be part of a geographically distributed (Amsterdam/Lisbon/Milan) team that embraces remote and hybrid collaboration. What you'll be doing Collaborate closely with ML Engineers, Data Scientists, and various engineers across Mollie. Contribute to the design, development, and maintenance of our scalable, low-latency cloud ML Platform and services. Deploy ML models to production in partnership with Data Scientists. Promote and implement best practices and standards in MLOps. Document MLOps workflows and architecture, ensuring compatibility with other systems at Mollie, including compliance with security standards (e.G., threat modeling). What you'll bring 1-3 years of proven experience as an ML Engineer (or similar role), including developing and maintaining ML Platforms and Model Pipelines in production environments. Advanced software engineering skills with a passion for coding in Python. Familiarity with at least one major cloud ML platform, preferably Google Cloud's Vertex AI. Experience with containers and container orchestration, such as Docker, Kubernetes, and Kubeflow. Proficiency with Terraform or similar infrastructure-as-code (IaC) tools. Comfort with using a Linux shell and Git for version control. Knowledge of the Model Development Lifecycle and common DS libraries, such as scikit-learn, pandas, shap, feature-engine, xgboost (or lgbm), and MLflow. Attention to detail with the ability to quickly shift priorities when required. Strong presentation skills and the ability to communicate effectively with diverse audiences. Enjoy working collaboratively in a cross-functional and distributed team environment. Comfort with Agile methodologies, such as Scrum, Kanban, or similar frameworks. Nice to have: Experience with LLMOps, i.E. deploying and managing GenAI solutions in production. Experience with (Py)Spark and managing Spark clusters. Experience in the financial services industry (banking or fintech). M.Sc. in Machine Learning, Computer Science, or Computer Engineering
