Lead Software Engineer (Full stack)

Mastercard is hiring a Lead Software Engineer (Full stack) in Mexico City, Mexico. Level rates it ; you can apply on Mastercard.

Level's read on this job

On Level's scale, this software engineer role is Builds AI ●●●●: the job is building AI systems.
Level classes it as a lead role.
Mastercard has 948 other open roles on Level.

AI in this role

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

Lead Software Engineer (Full stack)

Overview
The CNPF Data & AI organization 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 lead the design and delivery of secure, scalable, and reliable agentic applications that can reason, orchestrate tools, interact with enterprise systems, and deliver measurable business value. 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

Key Responsibilities
• Lead hands-on architecture, design, and implementation of agentic applications, AI-powered services, and platform capabilities from concept through production
• 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
• Provide technical leadership through architectural decisions, design reviews, code reviews, hands-on contribution, and mentoring of engineers across the team
• 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
• Collaborate across teams and business units to solve complex business and engineering problems with practical, high-impact solution
• Keep senior stakeholders informed of progress, risks, trade-offs, and implementation decisions in a clear and concise manner

All about you
• Bachelor’s degree in computer science, Software Engineering, or a related technical field; advanced degree preferred.
• + 10 years software engineering experience building scalable, secure, maintainable production systems, including experience leading complex technical initiatives end to end
• Hands-on experience building and shipping AI-powered products or agentic applications using LLMs, orchestration frameworks, tool-calling patterns, retrieval, and context-aware workflows
• Strong understanding of agentic system design, including planning, reasoning loops, workflow orchestration, memory, grounding, evaluation, safety, and human-in-the-loop controls
• Experience taking AI solutions from prototype to production with sound engineering discipline around reliability, observability, latency, cost, security, and governance
• Experience with modern AI frameworks, SDKs, and tooling for building AI applications, agent workflows, and developer productivity use cases
• Strong programming skills in one or more backend languages such as Java or 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, or GCP
• Good understanding of APIs, distributed systems, event-driven architectures, data pipelines, and integration patterns across enterprise platforms
• Experience with CI/CD, automated testing, DevSecOps, 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, authorization, secrets management, encryption, threat modelling, and secure deployment practices for AI-enabled systems
• Proven ability to create reusable platforms, frameworks, or internal engineering capabilities that improve developer experience and accelerate delivery across teams
• Strong product mindset with the ability to translate user needs and business goals into practical, high-impact technical solutions
• Excellent collaboration and communication skills, with experience influencing across engineering, product, data science, and leadership stakeholders

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.

How we rate this

Lead Software Engineer (Full stack) at Mastercard rates 84 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.