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
Director, Software Engineering
Overview The CNPF Data & AI organization is looking for a "Director, Software Engineering” to drive hands on delivery of applied AI and agentic capabilities, building the Mastercard Virtual C-Suite products. You will lead by example through deep hands-on engineering, influence technical direction, and partner closely with Applied AI, Data Science, and Product teams to take agentic solutions from experimentation to secure, scalable production.
The Director, Software Engineering is responsible for defining and delivering the engineering strategy for next-generation agentic applications, reusable agent frameworks, and enterprise AI enablement capabilities. This leader will build and scale software platforms where agents operate as intelligent personas, embedded product capabilities, and orchestrated digital workers that solve meaningful customer and enterprise problems. The role requires a strong engineering leader who can translate emerging AI and agentic concepts into secure, reliable, observable, and production-grade systems, while also accelerating AI tooling adoption and modern engineering practices across the organization.
We Relentlessly Care About the What and About the How • You make sound decisions with incomplete information, take ownership of outcomes, and adapt as circumstances evolve: Comfort with ambiguity • The standards are high and the scope is broad. You pursue depth with genuine intellectual curiosity and apply rigorous judgment about where that depth is most needed: Curiosity and prioritization • You invest in your teammates. You share what you learn. You measure your contribution by the strength of the team, not only by individual performance: Giver's mindset • You understand that the strength of this team is inseparable from the diversity of thought, background, and experience each member brings. You contribute to a culture where different perspectives are actively sought, and where collective resilience matters as much as individual excellence: Cultural fit and team spirit
Key Responsibilities • Define and lead the engineering vision for agentic applications, agent frameworks, and reusable AI platform capabilities that can be adopted across multiple products and teams. • Build and scale engineering teams that create agents as product capabilities, agent personas for targeted workflows, and orchestration layers that support multi-step reasoning, tool use, and action execution. • Drive the architecture and delivery of production-grade agentic systems, including context management, memory, tool integration, workflow orchestration, observability, evaluation, and safety guardrails. • Establish a common agentic engineering framework that standardizes how teams design, build, test, deploy, monitor, and improve intelligent agents at enterprise scale. • Partner with Product, Data Science, Security, Risk, and Platform teams to identify high-value use cases and translate them into scalable software solutions. • Lead the adoption of AI-assisted engineering and developer tooling that improves productivity, code quality, speed of experimentation, and software delivery outcomes. • Shape platform capabilities for agent lifecycle management, prompt and instruction management, model access, retrieval integration, policy enforcement, telemetry, and feedback loops. • Drive engineering excellence across architecture, coding standards, testing, release governance, reliability, incident response, and cost-aware operations for AI-native systems. • Define measurable success criteria for agentic products and platforms, including adoption, quality, accuracy, latency, resilience, developer experience, and business impact. • Create reusable patterns, reference architectures, and golden paths that enable teams to move from experimentation to governed production delivery faster. • Represent engineering leadership in executive reviews and cross-functional forums, clearly communicating technical trade-offs, delivery progress, risks, and strategic opportunities. All About You The ideal candidate is a strong technical and organizational leader who can connect strategy, platform thinking, and hands-on engineering execution. You are energized by building the foundations for agentic software systems and turning AI capabilities into practical, secure, and scalable products. You understand that agents are not only assistants, but can also be designed as personas, workflow participants, and product capabilities that must be governed, measured, and continuously improved. You bring a strong bias for action, sound engineering judgment, and the ability to build high-performing teams that move quickly without compromising quality, trust, or enterprise standards. You are equally comfortable shaping long-term platform direction, guiding architecture and delivery decisions, and enabling broad AI adoption through strong tooling, patterns, and developer experience.
Qualifications • Bachelor’s degree in computer science, Software Engineering, or a related technical field; advanced degree preferred.
- 15 years of experience leading software engineering organizations, including managers and multiple cross-functional teams, in product, platform, or enterprise technology environments. • Strong track record of building scalable, secure, resilient distributed systems and cloud-native platforms used across multiple teams or business domains. • Hands-on experience building or scaling Generative AI, LLM-powered applications, AI agents, workflow automation, or adjacent intelligent software systems. • Deep understanding of agentic engineering concepts such as tool calling, orchestration, planning, memory, context handling, retrieval, evaluation, observability, and human-in-the-loop controls. • Experience creating reusable engineering frameworks, platform services, or internal developer capabilities that accelerate enterprise adoption of modern technologies. • Strong software engineering foundation across modern application architectures, APIs, microservices, event-driven systems, and cloud platforms such as AWS, Azure, or Kubernetes-based environments. • Demonstrated ability to balance innovation with governance, including security, privacy, reliability, risk management, and responsible AI practices. • Experience driving AI tooling adoption or engineering productivity initiatives that improve developer effectiveness and software delivery outcomes. • Strong executive communication and stakeholder management skills, with the ability to influence strategy and explain complex technical trade-offs clearly.
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.