Overview Mastercard is a global technology company in the payments industry, connecting and powering an inclusive, digital economy that benefits everyone, everywhere by making transactions safe, simple, smart, and accessible. Job Description We are looking for a qualified MLOps Engineering Manager to join the AI Fraud Solutions team to support the delivery of models used in products and services that protect the payments ecosystem against fraud. The position involves designing the monitoring strategy of offline AI models deployed in production, ensuring our systems are observable and we can react swiftly to any issues. Additionally, the role includes supporting model refreshes, 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. Key Responsibilities: - 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 - Communicating with stakeholders to build requirements and track progress against issues that may arise - Developing systems for data versioning, model management, and deployment strategies, ensuring that models are easy to manage, debug, and deploy Requirements The ideal candidate will have proven experience building data pipelines as an ML DevOps Engineer, Data Engineer, or similar role, strong proficiency with open-source tools, containerization, and orchestration platforms (e.g., Kubernetes), along with experience in data versioning and model management tools. Additionally, the candidate should have experience working with various database systems (e.g., SQL, NoSQL) and ability to translate business requirements into technical specifications. Exposure to machine learning methodologies, best practices, modeling approaches, and frameworks is also required, along with 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). Strong experience with Kubernetes, Spark, and Hadoop for managing and processing large-scale data, proficiency in Hive, Impala, and SQL for efficient data querying and manipulation, and solid experience with Python for scripting, automation, and data processing tasks are necessary. Experience working in cross-functional teams to execute projects, strong organizational skills with the ability to learn quickly and manage multiple projects in a fast-paced environment, and previous experience in a data scientist role or similar capacity are also beneficial. A Bachelor's or Master's degree in engineering, computer science, or a related field (or equivalent professional experience) is required. Benefits At Mastercard, we provide a comprehensive package of benefits and perks that cater to different needs and interests. We believe that our employees are our greatest asset, and we strive to create a work environment that fosters growth, collaboration, and innovation. Our benefits include access to cutting-edge technology, opportunities for professional development, a competitive salary, and a range of other perks and benefits designed to enhance your overall well-being. Career Opportunities As a Machine Learning DevOps Engineer with a focus on MLOps at Mastercard, you will be part of a team that is shaping the future of payment systems and innovations in the fintech industry. You will have the opportunity to work on challenging projects, collaborate with talented professionals from diverse backgrounds, and contribute to the development of cutting-edge solutions that transform the way people make payments and conduct financial transactions.