Our Purpose Mastercard powers economies and empowerspeople in 200+ countries and territories worldwide. Together withour customers, we’re helping build a sustainable economy whereeveryone can prosper. We support a wide range of digital paymentschoices, making transactions secure, simple, smart and accessible. Our technology and innovation, partnerships and networks combine todeliver a unique set of products and services that help people,businesses and governments realize their greatest potential. Titleand Summary MLOps Engineering Manager (SSO10004) Overview Who is Mastercard? Mastercard is a global technology company in thepayments industry. Our mission is to connect and power aninclusive, digital economy that benefits everyone, everywhere bymaking transactions safe, simple, smart, and accessible. Usingsecure data and networks, partnerships and passion, our innovationsand solutions help individuals, financial institutions,governments, and businesses realize their greatest potential. Ourdecency quotient, or DQ, drives our culture and everything we doinside and outside of our company. With connections across morethan 210 countries and territories, we are building a sustainableworld that unlocks priceless possibilities for all. Services within Mastercard is responsible for acquiring, engaging, and retainingcustomers by managing fraud and risk, enhancing cybersecurity, andimproving the digital payments experience. We provide value-addedservices and leverage expertise, data-driven insights, andexecution. The Security Solutions team at Mastercard deliverstechnology, products, and services that facilitate seamless, fastand secure payments across the network and the internet of things(IoT). These solutions leverage the latest technology, a vast arrayof data resources, and artificial intelligence to provide servicesthat benefit the entire payments ecosystem. Role We are looking fora qualified MLOps Engineering Manager to join the AI FraudSolutions team to support the delivery of models used in productsand services that protect the payments ecosystem against fraud. This position is responsible for building pipelines to supportmonitoring, reporting any issues to appropriate engineering or datascience teams, highlighting any model degradation and creatingreports for customer delivery. Building the machine learningmonitoring infrastructure (or MLOps) is the biggest challenge mostlarge companies currently have in making the transition to becomingan AI-driven organization. This position is an opportunity for anexperienced engineer to build expertise in this exciting newfrontier. You will be part of a team monitoring state-of-the-art MLsystems that power the global payment network at Mastercard. Keyresponsibilities include: 1. Designing the monitoring strategy ofoffline AI models deployed in production - ensure that our systemsare observable and that we can react swiftly to any issues. 2. Supporting model refreshes to ensure models are deployed correctlyin production. 3. Creating deployment reports for key stakeholdersand customers. 4. Assisting in the deployment of scalable tools andservices to handle machine learning training and inference inproduction. 5. Evaluating new technologies to improve performance,maintainability, and reliability of our machine learning systems –including facilitating proof-of-concepts. 6. Communicating withstakeholders to build requirements and track progress againstissues that may arise. 7. Developing systems for data versioning,model management, and deployment strategies, ensuring that modelsare easy to manage, debug, and deploy. All About You 1. Provenexperience building data pipelines as an ML DevOps Engineer, DataEngineer, or similar role. 2. Strong proficiency with open-sourcetools, containerization, and orchestration platforms (e.g.,Kubernetes), along with experience in data versioning and modelmanagement tools. 3. Experience working with various databasesystems (e.g., SQL, NoSQL). 4. Ability to translate businessrequirements into technical specifications. 5. Exposure to machinelearning methodologies, best practices, modeling approaches, andframeworks. 6. Hands-on experience with machine learning frameworksand libraries such as Scikit-learn (Sklearn), Pandas, Numpy,XGBoost, LightGBM, CatBoost, and deep learning frameworks (PyTorch,TensorFlow). 7. Strong experience with Kubernetes, Spark, and Hadoop for managing and processing large-scale data. 8. Proficiencyin Hive, Impala, and SQL for efficient data querying andmanipulation. 9. Solid experience with Python for scripting,automation, and data processing tasks. 10. Experience working incross-functional teams to execute projects. 11. Strongorganizational skills with the ability to learn quickly and managemultiple projects in a fast-paced environment. 12. Previousexperience in a data scientist role or similar capacity is a plus.13. Bachelor’s or Master’s degree in engineering, computer science,or a related field (or equivalent professional experience). Corporate Security Responsibility All activities involving accessto Mastercard assets, information, and networks comes with aninherent risk to the organization and, therefore, it is expectedthat every person working for, or on behalf of, Mastercard isresponsible for information security and must: - Abide by Mastercard’s security policies and practices; - Ensure theconfidentiality and integrity of the information being accessed; -Report any suspected information security violation or breach, and- Complete all periodic mandatory security trainings in accordancewith Mastercard’s guidelines. #J-18808-Ljbffr Engineering