About Us Mastercard powers economies and empowers people in 200+ countries and territories worldwide. Together with our customers, we're helping build a sustainable economy where everyone can prosper. Why Join Us? We are a global technology company that is leading the charge in payments innovation. At Mastercard, we believe in the power of technology to create a more inclusive, equitable, and connected world. If you share our passion for innovation, customer-centricity, and sustainability, we invite you to join our team and be part of a collaborative and dynamic work environment that encourages creativity, diversity, and growth. As a Machine Learning DevOps Engineer with a focus on MLOps, you will have the opportunity to work on exciting projects, collaborate with talented professionals, and contribute to the development of cutting-edge solutions that shape the future of payment systems and innovations in the fintech industry. Responsibilities Your primary responsibilities as a Machine Learning DevOps Engineer with a focus on MLOps at Mastercard will include designing the monitoring strategy of offline AI models deployed in production, supporting model refreshes to ensure models are deployed correctly in production, creating deployment reports for key stakeholders and customers, assisting in the deployment of scalable tools and services to handle machine learning training and inference in production, evaluating new technologies to improve performance, maintainability, and reliability of our machine learning systems, and communicating with stakeholders to build requirements and track progress against issues that may arise. Additionally, you will develop systems for data versioning, model management, and deployment strategies, ensuring that models are easy to manage, debug, and deploy. Requirements To succeed in this role, you will need to have a strong foundation in machine learning, software development, and DevOps. This includes experience building data pipelines as an ML DevOps Engineer, Data Engineer, or similar role, proficiency with open-source tools, containerization, and orchestration platforms (e.g., Kubernetes), and experience in data versioning and model management tools. Additionally, you should have hands-on experience with machine learning frameworks and libraries such as Scikit-learn (Sklearn), Pandas, Numpy, XGBoost, LightGBM, CatBoost, and deep learning frameworks (PyTorch, TensorFlow). Experience working with various database systems (e.g., SQL, NoSQL), ability to translate business requirements into technical specifications, exposure to machine learning methodologies, best practices, modeling approaches, and frameworks, and strong organizational skills with the ability to learn quickly and manage multiple projects in a fast-paced environment are also required. Candidate Profile We are looking for a highly motivated and experienced Machine Learning DevOps Engineer with a focus on MLOps who has a strong background in machine learning, software development, and DevOps. You should have a proven track record of building data pipelines, experience working with various database systems, and hands-on experience with machine learning frameworks and libraries. Additionally, you should be able to communicate complex technical concepts to non-technical stakeholders and have strong organizational skills with the ability to learn quickly and manage multiple projects in a fast-paced environment.