Our Purpose
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. We support a wide range of digital payments choices, making transactions secure, simple, smart and accessible. Our technology and innovation, partnerships and networks combine to deliver a unique set of products and services that help people, businesses and governments realize their greatest potential.
Title and Summary
MLOps Engineering Manager (SSO10004)
Overview
Who is Mastercard?
Mastercard is a global technology company in the payments industry. Our mission is to connect and power an inclusive, digital economy that benefits everyone, everywhere by making transactions safe, simple, smart, and accessible. Using secure data and networks, partnerships and passion, our innovations and solutions help individuals, financial institutions, governments, and businesses realize their greatest potential.
Our decency quotient, or DQ, drives our culture and everything we do inside and outside of our company. With connections across more than 210 countries and territories, we are building a sustainable world that unlocks priceless possibilities for all.
Services within Mastercard is responsible for acquiring, engaging, and retaining customers by managing fraud and risk, enhancing cybersecurity, and improving the digital payments experience. We provide value-added services and leverage expertise, data-driven insights, and execution.
The Security Solutions team at Mastercard delivers technology, products, and services that facilitate seamless, fast and secure payments across the network and the internet of things (IoT). These solutions leverage the latest technology, a vast array of data resources, and artificial intelligence to provide services that benefit the entire payments ecosystem.
Role
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. This position is responsible for building pipelines to support monitoring, reporting any issues to appropriate engineering or data science teams, highlighting any model degradation and creating reports for customer delivery.
Building the machine learning monitoring infrastructure (or MLOps) is the biggest challenge most large companies currently have in making the transition to becoming an AI-driven organization. This position is an opportunity for an experienced engineer to build expertise in this exciting new frontier. You will be part of a team monitoring state-of-the-art ML systems that power the global payment network at Mastercard.

Key responsibilities include:

• Designing the monitoring strategy of offline AI models are deployed in production - ensure that our systems are observable and that we can react swiftly to any issues
• 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 – including facilitating proof-of-concepts
• 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

All About You

• 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.
• 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.
• 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.
• Solid experience with Python for scripting, automation, and data processing tasks.
• 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.
• Previous experience in a data scientist role or similar capacity is a plus.
• Bachelor’s or Master’s degree in engineering, computer science, or a related field (or equivalent professional experience)

Corporate Security Responsibility

All activities involving access to Mastercard assets, information, and networks comes with an inherent risk to the organization and, therefore, it is expected that every person working for, or on behalf of, Mastercard is responsible for information security and must:

• Abide by Mastercard’s security policies and practices;
• Ensure the confidentiality and integrity of the information being accessed;
• Report any suspected information security violation or breach, and
• Complete all periodic mandatory security trainings in accordance with Mastercard’s guidelines.

Key responsibilities include:

• Designing the monitoring strategy of offline AI models are deployed in production - ensure that our systems are observable and that we can react swiftly to any issues
• 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 – including facilitating proof-of-concepts
• 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 deplo

All activities involving access to Mastercard assets, information, and networks comes with an inherent risk to the organization and, therefore, it is expected that every person working for, or on behalf of, Mastercard is responsible for information security and must:

• Abide by Mastercard’s security policies and practices;
• Ensure the confidentiality and integrity of the information being accessed;
• Report any suspected information security violation or breach, and
• Complete all periodic mandatory security trainings in accordance with Mastercard’s guidelines