Data Engineer ( AI/ML )

Job description

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 Data Engineer ( AI/ML ) Overview- Mastercard is a global technology company powering one of the world’s fastest payment networks. Our Data Warehouse enables data-driven insights that help customers solve complex business challenges. In this role, you will contribute to a growing organization, collaborating with skilled engineers to deliver innovative and impactful data solutions. This role is seeking a Machine Learning / AI Data Engineer to support the development, deployment, and operationalization of data science models at scale. This role combines strong data engineering fundamentals with AI/ML expertise to build scalable data platforms, optimize model pipelines, and enable production-ready AI solutions. The ideal candidate will have hands-on experience with Python, SQL, PySpark, cloud technologies, MLOps, and CI/CD practices. Role - • Design, develop, and maintain scalable data pipelines and cloud-based data platforms supporting AI and ML workloads. • Build, deploy, and optimize machine learning models and AI solutions for enterprise-scale applications. • Develop robust data processing and feature engineering solutions using Python, SQL, and PySpark. • Support end-to-end ML lifecycle management, including model deployment, monitoring, automation, and governance. • Implement and maintain CI/CD pipelines for data and machine learning applications. • Collaborate with data scientists, engineers, and business stakeholders to operationalize AI/ML solutions. • Ensure data quality, scalability, reliability, and performance across data and ML platforms. All About You - • 5–6 years of experience in Data Engineering, Machine Learning Engineering, or AI-related roles. • Strong programming expertise in Python. • Expert-level SQL skills, including data modeling, query optimization, performance tuning, and complex data transformations. • Strong hands-on experience with PySpark and distributed data processing. • Experience developing, deploying, and supporting machine learning models in production environments. • Solid understanding of AI/ML concepts, including Generative AI, LLMs, RAG architectures, AI agents, prompt engineering, and model lifecycle management. • Experience building and maintaining scalable ETL/ELT pipelines, data lakes, and cloud-based data platforms. • Hands-on experience with cloud platforms such as AWS, Azure, or GCP. • Strong understanding of MLOps, including model deployment, monitoring, versioning, automation, and governance. • Experience implementing CI/CD pipelines and DevOps best practices. • Knowledge of containerization, orchestration, and cloud-native architectures. • Strong analytical, problem-solving, and communication skills. • Experience working in Agile/Scrum environments. • Ability to collaborate effectively with data engineers, software engineers, data scientists, and business stakeholders. • Experience integrating AI/ML capabilities into enterprise data platforms and business applications. • Understanding of data governance, model governance, security, and responsible AI practices. 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.