The team is looking for a Lead Software Engineer to help build the next generation of intelligent, agentic products and platforms powering the Mastercard Virtual C-Suite. This is a hands‐on technical leadership role for an experienced engineer who combines strong software engineering fundamentals with practical experience building production‐ready AI systems.
You will work closely with Applied AI, Data Science, Product, Security, and Platform teams to move from concept to experimentation to governed production deployment.
This role will suit a builder who enjoys solving complex problems, working across disciplines, and helping teams deliver high‐quality software at pace. We are particularly interested in engineers who know how to use AI responsibly both within products and across the software development lifecycle to improve quality, productivity, engineering effectiveness, and delivery outcomes.
Define engineering patterns and best practices for production AI systems, including evaluation, monitoring, guardrails, resiliency, cost control, and rollback strategies.
Drive end‐to‐end software delivery across the SDLC, from discovery and prototyping to testing, release, and production operations.
Use engineering tools to accelerate design, coding, testing, documentation, troubleshooting, and delivery while maintaining strong engineering judgment and code quality standards.
Champion an AI‐enabled SDLC by improving developer workflows, automation, test generation, code review quality, release confidence, and team productivity.
Partner closely with Product, Applied AI, Data Science, and business stakeholders to translate ambiguous opportunities into scalable product capabilities.
Build highly available, secure, and maintainable cloud‐native services with strong observability, performance, and operational readiness.
Shape technical roadmaps, identify short‐ and long‐term platform needs, and influence architecture choices that enable scale, reuse, and faster delivery.
Keep senior stakeholders informed of progress, risks, trade‐offs, and implementation decisions in a clear and concise manner.
Strong software engineering experience building scalable, secure, maintainable production systems, including experience leading complex technical initiatives end to end.
Strong understanding of agentic system design, including planning, reasoning loops, workflow orchestration, memory, grounding, evaluation, safety, and human‐in‐the‐loop controls.
Strong programming skills in one or more backend languages such as Java and Python, with the ability to write high‐quality, well‐tested, production‐ready code.
Experience with modern front‐end frameworks such as React and/or Next.js for building intuitive product experiences would be beneficial.
Experience building services in cloud‐native environments using Kubernetes and managed cloud services on AWS, Azure.
Good understanding of APIs, distributed systems, event‐driven architectures, data pipelines, and integration patterns across enterprise platforms.
Experience with CI/CD, automated testing, and engineering automation, including the ability to improve SDLC efficiency and release quality using AI tools.
Practical experience using AI coding and engineering assistants to improve productivity across design, implementation, testing, debugging, documentation, and operational support.
Strong background in software security, including authentication, authorisation, secrets management, encryption, threat modelling, and secure deployment practices for AI‐enabled systems.
Excellent collaboration and communication skills, with experience influencing across engineering, product, data science, and leadership stakeholders.
Strong hands‐on programming expertise in Java and Python.
Strong experience with React for building modern, responsive, and intuitive user interfaces for enterprise applications.
Experience with Next.js or modern front‐end architecture patterns alongside React.
Deep experience building cloud‐native applications using containers, Kubernetes, microservices, and managed cloud services in AWS and/or Azure.
Practical experience using AI tools to improve engineering productivity across coding, testing, debugging, documentation, and release workflows.
Strong understanding of software engineering quality metrics such as code quality, test automation, reliability, performance, observability, and maintainability.
Experience with API gateway, service mesh, and enterprise integration patterns.
Experience with Kafka, event streaming platforms, or large‐scale messaging ecosystems.
You have experience building or operating AI‐enabled or agentic applications in production and understand what it takes to make them secure, reliable, and useful at scale.
You combine strong software engineering fundamentals with curiosity and good judgment in applying emerging AI capabilities to real business problems.
You actively use AI to enhance your own engineering productivity and help teams adopt better ways of designing, coding, testing, documenting, and operating software.
You understand where AI can accelerate delivery and where human review, engineering discipline, and thoughtful controls remain essential.
You care deeply about customer value, developer experience, quality, resilience, and long‐term maintainability.
You communicate complex technical concepts clearly and effectively to both engineering teams and senior stakeholders.
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