Senior Machine Learning Engineer II

About the role

You will be the dedicated technical leader for the Operations AI team, focused on designing, developing, and deploying high-performance, scalable ML/AI solutions in production environments. This role is critical for developing and deploying the next generation of AI-driven solutions that automate support and drastically improve the experience for both merchants and support agents.

What you'll do

• Architect, design, develop, and deploy AI solutions and systems into production environments, ensuring reliability, high performance, and scalability.
• Take ownership and technical leadership in the development and maintenance of main AI products, including the AI Assistant, AI Translation, and the AI Agent Copilot.
• Develop and maintain ML infrastructure and pipelines to support efficient data processing, model training, serving, and continuous monitoring.
• Optimise and fine-tune machine learning models, and collect, preprocess, and clean large text datasets.
• Embrace software development principles and best practices, including version control (Git), CI/CD processes (GitHub actions), and unit testing frameworks.
• Collaborate closely with Data Scientists, Product Managers, Developers, and other business stakeholders to enhance customer experience and improve operational efficiency globally.
• Lead the creation of the pilot for the Voice Assistant and design how to leverage current AI assistant components to empower new channels.

What you'll need

• 7+ years of proven experience as a Machine Learning Engineer or AI Engineer, focusing on building and deploying scalable machine learning or AI solutions.
• Expert-level Python proficiency and excellent software development engineering skills for designing computationally effective solutions in large-scale production.
• Strong previous experience with models in production, and a strong understanding of artificial intelligence, machine learning, and deep learning concepts.
• Experience using GenAI / LLM API and experience evaluating LLM models in production.
• Experience building and deploying ML Models using Cloud platforms (AWS, GCP, or Azure).
• Familiarity with MLOps tools (e.g., MLFlow, Kubeflow, Airflow, Langfuse), and hands-on experience with Kubernetes, Git, and Airflow.
• Experience building, maintaining, and scaling APIs, and deployment using CI/CD Pipelines in GitHub actions.

What they offer

• Virtual Stock Option programme to own a stake in SumUp's future success.
• Dedicated annual L&D budget of €2000 for attending conferences and advancing your career.
• Corporate pension scheme where SumUp matches up to 20% of your contributions.
• Generous time off: 28 days of paid leave plus public holidays and special leave days.
• 1-month sabbatical after 3 years of service.
• Urban Sports Club subsidy, Kita placement assistance, subsidised office lunches, and referral bonus.