Description
Mastercard's CNPF Data & AI organization is seeking an exceptional Senior Software Engineer to build the next generation of intelligent, agentic applications and engineering platforms.
As a Senior Software Engineer, you will design and build scalable, production-grade systems that leverage AI, agentic workflows, and modern developer tooling to accelerate product delivery and improve engineering productivity across the full product development lifecycle.
Responsibilities:
- 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.
Requirements:
- Strong 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.
- Strong 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.
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.