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 CNPF Data & AI organization is looking for an exceptional Software Engineer II to help build the next generation of intelligent, agentic applications and engineering platforms. This is a high-impact role for a hands-on engineer who combines deep software engineering expertise with a passion for AI, innovation, and practical problem solving.

You will design and build scalable, production-grade systems that leverage AI, agentic workflows, and modern developers tooling to accelerate product delivery and improve engineering productivity across the full product development lifecycle. You will work at the intersection of software engineering, AI capability development, developer experience, and platform innovation—turning emerging technologies into secure, reliable, and reusable capabilities that create measurable business value.

Position Responsibilities

As a Software Engineer II, you will:

Software Design, Architecture & Development:

• Design, build, and evolve intelligent, agentic applications and platform capabilities that solve meaningful business problems at scale
• Apply robust software design principles, design patterns, and architectural best practices to create scalable, maintainable, and extensible systems
• Influence and contribute to system architecture decisions, including distributed systems, event-driven patterns, and cloud-native design
• Develop high-quality, well-structured, and efficient code leveraging strong data structures and algorithmic thinking

AI-Enabled Engineering & Innovation:

• Apply AI and agentic development patterns to improve engineering productivity across design, coding, testing, debugging, documentation, release engineering, and operational support
• Use modern tools and coding assistants thoughtfully to accelerate delivery while maintaining strong standards for quality, security, reliability, and maintainability
• Evaluate emerging AI tools, frameworks, and platforms and integrate them into engineering workflows where they create measurable value

Testing & Quality Engineering:

• Design and implement comprehensive testing strategies, including unit, functional, integration, and end-to-end testing
• Build and maintain automated testing frameworks and integrate them into CI/CD pipelines
• Ensure high levels of test coverage, system reliability, and defect prevention across the software lifecycle
• Leverage AI-assisted solutions to enhance testing efficiency and effectiveness

Code Quality & Engineering Excellence:

• Conduct and actively participate in code reviews, ensuring adherence to coding standards, maintainability, security, and best practices
• Champion engineering excellence, including clean code, reusable design, and continuous improvement practices
• Mentor peers through review processes and knowledge sharing

Performance, Security & Reliability:

• Design and optimize systems for performance, scalability, latency, and cost efficiency
• Apply and promote secure coding practices, integrating security considerations into all stages of development (DevSecOps)
• Ensure systems are observable, resilient, and production-ready, with strong monitoring, alerting, and incident response capabilities

Operability & Troubleshooting:

• Build systems with strong operational readiness, ensuring maintainability and supportability in production environments
• Diagnose and resolve complex technical issues across distributed systems, leveraging structured troubleshooting approaches
• Continuously improve system reliability through automation, root cause analysis, and proactive enhancements

Collaboration & Delivery:

• Partner with product, data science, and engineering teams to translate ideas into production-grade solutions with clear business impact
• Improve SDLC efficiency through automation, CI/CD, and AI-assisted workflows
• Lead by example through hands-on development, strong ownership, and execution

All About You

• You thrive on building innovative, scalable systems that solve real-world problems
• You combine strong engineering fundamentals (design, testing, algorithms) with curiosity for emerging AI technologies
• You take ownership of outcomes and consistently deliver high-quality solutions
• You balance speed with engineering rigor, ensuring systems are secure, reliable, and maintainable
• You are a strong collaborator who elevates team performance through communication, mentorship, and shared standards
• You embrace continuous learning and experimentation to push the boundaries of modern software engineering

Ideal Candidate Qualifications

• Proficient programming skills in languages such as Java and/or Python, with demonstrated ability to write efficient, high-performance code using appropriate data structures and algorithms
• Hands-on experience building agentic or AI-enabled applications;
• Proficient understanding of APIs, distributed systems, event-driven architectures, and enterprise integration patterns
• Intermediate experience with React or Next.js
• Experience with cloud-native development using Kubernetes and managed cloud platforms such as AWS or Azure
• Experience with CI/CD, automation, and engineering productivity tooling;
• Familiarity with modern AI frameworks, SDKs, and tools for building intelligent applications and agent workflow
• Proven experience building scalable, maintainable, production-grade systems
• Proven experience designing and implementing unit, functional, and integration testing strategies and frameworks
• Hands-on experience building applications or platforms using Generative AI or agentic design patterns
• Strong understanding of:
• Distributed systems and cloud-native architectures
• APIs, event-driven systems, and integration patterns
• AI system design trade-offs (latency, cost, reliability, safety, governance)
• Experience driving code quality through peer code reviews and engineering standards enforcement
• Experience taking AI-enabled solutions from prototype to production with strong engineering discipline across reliability, observability, performance, and security
• Experience improving SDLC through:
• CI/CD pipelines
• Automation and testing frameworks
• AI-assisted engineering workflows
• Experience with Kubernetes and cloud platforms (AWS, Azure)
• Familiarity with AI frameworks, SDKs, and tools (LLMs, RAG, embeddings, inference APIs)

Preferred Qualifications

• Familiarity with model evaluation, AI safety controls, and governance practices
• Experience building reusable developer platforms or internal engineering tools
• Practical use of AI coding assistants to improve delivery speed and quality;
• Familiarity with LLM orchestration, prompt and context management, memory patterns, inference APIs, embeddings, retrieval-augmented generation, vector databases, model evaluation, AI observability, and responsible AI controls.

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.