Lead AI Platform Engineer

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 AI Platform Engineer Overview As a Lead AI Platform Engineer within Mastercard's AI Center of Excellence, you will lead the design, development, and evolution of the enterprise AI platform that enables teams to build, deploy, and operate AI and Generative AI solutions at scale. You will combine deep technical expertise with engineering leadership, managing a team of AI Engineers while establishing the platform capabilities, standards, and operational practices that accelerate AI adoption across the enterprise You will partner with Enterprise Architecture, Cloud Engineering, Data Engineering, Security, Product Management, and Data Science teams to deliver a secure, scalable, and reusable AI ecosystem supporting the full AI lifecycle from experimentation through production Key Responsibilities - Lead the design and evolution of Mastercard's enterprise AI platform, providing reusable services, tools, and frameworks that accelerate AI solution delivery - Manage and mentor a team of AI Engineers, providing technical guidance, coaching, performance feedback, and career development - Define the technical roadmap for AI platform capabilities, ensuring alignment with business priorities and enterprise technology strategy - Build and maintain shared AI services including model serving, inference APIs, feature stores, vector databases, prompt management, AI gateways, model registries, and developer self-service capabilities - Establish enterprise standards for MLOps and LLMOps, including CI/CD, model lifecycle management, observability, evaluation, governance, security, and automated deployment - Design cloud-native AI infrastructure using Kubernetes, containers, infrastructure as code, microservices, and event-driven architectures - Ensure the AI platform delivers high availability, scalability, resilience, performance, and cost optimization for enterprise workloads - Partner with AI Engineers and Data Scientists to productionize AI and Generative AI solutions, reducing time-to-market through standardized platform capabilities - Collaborate with Security, Risk, and Compliance teams to implement Responsible AI, governance, access controls, auditability, and regulatory requirements - Drive platform reliability through monitoring, incident management, capacity planning, disaster recovery, and operational excellence - Evaluate emerging AI technologies and integrate new platform capabilities that improve developer productivity and enterprise AI adoption - Foster engineering excellence by promoting software engineering best practices, automation, documentation, and continuous improvement across the AI engineering organization Required Qualifications - Bachelor's or Master's degree in Computer Science, Software Engineering, Artificial Intelligence, or a related field. - Extensive experience designing and operating enterprise software platforms, cloud-native applications, or AI/ML platforms. - Experience leading and mentoring engineering teams in an enterprise environment. - Strong software engineering skills using Python - Deep expertise with Kubernetes, Docker, APIs, microservices, Infrastructure as Code, and cloud platforms including AWS, Azure, or Google Cloud Platform - Experience building and operating AI/ML platforms, MLOps, or AgenticOps capabilities supporting production AI workloads - Experience with modern AI technologies including LLMs, RAG, vector databases, model serving frameworks, and AI