Software Engineer II

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 Software Engineer II Overview The Franchise and Legal Solutions (FLS) Program enables growth in Mastercard’s products, markets, services, and innovations while protecting our brand and business in an increasingly complex and competitive environment. The team supports and protects the franchise agreement by implementing the rules of the road for anyone interacting with our business. As a Software Engineer II, you will combine strong software engineering fundamentals with modern enterprise platforms, automation, data, and AI-enabled engineering to solve complex business problems. You will work across Java/Spring Boot applications, APIs and integrations, cloud platforms, Microsoft Power Platform, and generative AI technologies. What You'll Do Software Development • Design, build, test, deploy, and support software solutions for medium-sized initiatives and defined components of larger enterprise programs. • Develop applications, microservices, REST APIs, and system integrations using Java/J2EE, Spring Boot, and related technologies. • Apply data structures, algorithms, design patterns, and software engineering principles to solve business and technical problems. • Build automation and business solutions using Microsoft Power Platform, including Power Apps, Power Automate, and Dataverse. • Create data and analytics solutions using SQL, data modeling, Power BI, and related technologies. • Use Python and/or TypeScript for automation, application development, data processing, and AI integration. • Develop enterprise AI applications and workflows using LLMs, generative AI, RAG, and agentic patterns. • Apply AI-assisted development tools such as GitHub Copilot, Claude Code, or comparable coding agents within engineering workflows. • Integrate AI models with enterprise applications, APIs, tools, and data sources using technologies and patterns such as MCP servers and tool/API integration. • Build AI-ready data and retrieval pipelines using technologies such as Databricks, Spark, embeddings, vector search, and knowledge management. • Create and maintain technical and software development documentation. Design & Architecture • Partner with business and product owners to translate defined requirements into scalable, maintainable solutions. • Apply established software design and architecture principles, patterns, and standards across applications, services, integrations, data flows, and platform dependencies. • Determine whether traditional software development, low-code, analytics, automation, AI, or a combination is appropriate for defined problems. • Build cloud-native solutions and integrate secure, responsible AI capabilities into enterprise applications. • Design AI components using RAG, vector databases, LLM APIs, MCP/tool integration, and agentic frameworks, considering scalability, reliability, security, and maintainability. • Simplify technical approaches while balancing business value, complexity, performance, maintainability, and cost. Quality & Secure Engineering • Develop and maintain automated unit, functional, and integration tests using frameworks such as JUnit and Spock. • Apply secure coding, information security, web application security, and applicable PCI DSS principles throughout the software develo