Machine Learning Engineer

Job description

About Enfuce: Founded in 2016, Enfuce is a female-founded and led global payments partner solving payments complexity for ambitious brands, banks and fintech leaders. Born in the Nordics and built for the world, Enfuce combines deep payments expertise with cloud-native technology to help customers launch and scale with confidence. Enfuce is a strategic issuing and licensing partner delivering premium, flexible payment programmes across consumer, commercial and B2B use cases. Built for fast launch and trusted scale, Enfuce designs, launches and runs card programmes supporting debit, credit, prepaid, fleet and fuel propositions, alongside digital wallets and card scheme connectivity. Enfuce combines cloud-native issuing, embedded compliance and fraud intelligence to deliver velocity with control. Its modular technology and in-house expertise enable customers to create tailored payment solutions that are flexible, scalable and secure. As a regulated payments partner, Enfuce holds Electronic Money Institution licences from both the Finnish Financial Supervisory Authority and the UK Financial Conduct Authority and is a principal member of Visa and Mastercard. Its PCI-DSS-certified, cloud-native platform is designed for enterprise-grade reliability and global scalability, with 99.999% platform uptime. Enfuce has raised €73.5 million in funding and has received industry recognition including Fintech of the Year and Best ESG Campaign at the 2024 European Fintech Awards. About the role: As a Machine Learning Engineer at Enfuce, you will build and maintain the infrastructure, tooling, and platforms that enable machine learning and generative AI solutions to be developed, deployed, and operated reliably at scale. Working closely with Data Scientists and Data Engineers, you will own the production lifecycle of ML systems, from data pipelines and experiment tracking to model deployment, monitoring, and continuous delivery. You will help establish MLOps best practices across the organization by building reproducible machine learning workflows, scalable infrastructure, and automation that accelerates the delivery of AI-powered products. This role involves working with cloud-native technologies, modern MLOps platforms, and production-grade AI systems in the financial services domain. What you'll be doing: Design, build, and maintain scalable MLOps infrastructure for machine learning and Generative AI applications. Develop automated training, validation, testing, deployment, and CI/CD pipelines for machine learning models. Implement experiment tracking, model versioning, model registries, and artifact management using MLOps best practices. Build and maintain workflow orchestration, feature engineering, and data processing pipelines. Monitor production ML systems, including model performance, data quality, drift detection, latency, and overall system health. Manage the end-to-end model lifecycle, including retraining, rollback, reproducibility, governance, and auditability. Containerize ML workloads with Docker and deploy scalable services using cloud-native technologies and orchestration platforms. Develop and maintain Infrastructure as Code (IaC) for AI platforms and cloud resources. Collaborate with Data Scientists and software engineers to productionize, optimize, and scale machine learning solutions. Evaluate and implement new MLOps tools, frameworks, and best practices, including support for LLM and agentic AI applications. What you'll bring: Bachelor's or Master's degree in Computer Science, Machine Learning, Software Engineering, or a related field. Strong Python programming skills and proficiency with SQL. Experience with MLflow for experiment tracking, model registry, versioning, and model lifecycle management. Experience with modern ML platforms such as Snowflake, dbt, Snowpark ML, Vertex AI, or Amazon SageMaker. Strong understanding of the end-to-end machine learning lifecycle, including experimentation, deployment, monitoring, retraining, and governance. Experience with Git, software engineering best practices, and Infrastructure as Code (e.g., Terraform or CloudFormation). Experience with Docker, containerized ML workloads, and container orchestration platforms such as Kubernetes. Hands-on experience with cloud platforms such as AWS, Azure, or Google Cloud Platform, including production monitoring and observability. Familiarity with feature stores, model registries, artifact repositories, and modern MLOps practices. Experience deploying LLM or Generative AI applications is a strong advantage, along with excellent problem-solving, communication, and collaboration skills. Why You’ll Love Working At Enfuce High autonomy & ownership: We give you the freedom to own your work and trust you to make the best decisions for your projects. Top-tier talent: Join a team of industry experts and highly skilled professionals who are as passionate as you are about innovation. Unlimited growth potential: We support your ambition with plenty of room for personal and professional growth within the company. Flexible, remote work: Work from anywhere up to 30 days, in an environment that values flexibility and work-life balance. A supportive culture: You’ll be part of a team that encourages, motivates, and celebrates success together. Comprehensive benefits package: We take care of our people with great benefits to match the value you bring. Benefits & Perks: Fair pay and employee stock option: We value the input of every employee and want you to tap into the growth we build together. That’s why our salaries are competitive and reassessed regularly, and you have access to an employee stock option program. Flexible Paid Time Off: We offer a flexible paid time off policy, providing up to 5 weeks of annual vacation days and paid family leave, subject to country regulations. Additionally, you can benefit from hybrid or remote work options, promoting a healthy work-life balance. Regular Fun With Your Team: To spend other than work-related time with your teammates, you get a team activity budget for three quarters a year. The fourth quarter is reserved for a company-wide event. Healthcare, Mobile Phone And More: Depending on location, additional benefits can include healthcare, pension, phone support, lunch, wellbeing and insurance benefits.