Overview

Mastercard powers economies and empowers people in 200+ countries and territories worldwide.

Responsibilities

• Lead the architecture, design, and implementation of enterprise AI, machine learning, and Generative AI platforms and applications
• Drive the E2E delivery of production AI solutions, from solution architecture and engineering through deployment, monitoring, optimization, and lifecycle management
• Define enterprise AI engineering standards, reference architectures, reusable frameworks, and best practices for scalable AI development
• Lead the design and implementation of cloud-native AI platforms leveraging microservices, Kubernetes, APIs, event-driven architectures, and distributed systems
• Establish and evolve MLOps and AgenticOps capabilities, including automated CI/CD pipelines, model governance, observability, evaluation, security, and operational excellence
• Architect and deliver advanced Generative AI solutions LLMs, RAG, AI agents, vector databases, prompt engineering, and model orchestration frameworks
• Ensure AI systems meet enterprise requirements for scalability, resilience, security, compliance, Responsible AI, and operational reliability
• Partner with Enterprise Architecture, Platform Engineering, Data Science, Product Management, and Security teams to define AI technology roadmaps and implementation strategies
• Evaluate emerging AI technologies and drive innovation through proofs of concept, technology assessments, and enterprise adoption strategies
• Provide technical leadership and mentorship to AI engineers, fostering engineering excellence, knowledge sharing, and continuous improvement
• Lead technical design reviews, architecture governance, and engineering decision-making across AI initiatives

Qualifications

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Software Engineering or a related field
• Extensive experience designing, developing, and deploying enterprise-scale AI and machine learning solutions in production
• Demonstrated experience leading technical teams and delivering complex software engineering initiatives.
• Strong expertise in software engineering using Python
• Deep experience with cloud platforms such as AWS, Azure, or Google Cloud Platform, including cloud-native architecture and services
• Strong knowledge of distributed systems, microservices, APIs, containerization, Kubernetes, and infrastructure automation
• Proven experience implementing enterprise MLOps and AgenticOps practices, including model deployment, monitoring, governance, and continuous delivery
• Hands-on experience with modern AI frameworks such as PyTorch, TensorFlow, Scikit-learn, LangChain, LangGraph, LlamaIndex, or equivalent technologies
• Strong understanding of AI governance, Responsible AI, security, privacy, and regulatory compliance
• Excellent communication, stakeholder management, and technical leadership skills
• Enterprise AI platforms and applications that enable secure, scalable, and reusable AI capabilities across Mastercard
• Technical leadership that accelerates the adoption of AI while maintaining engineering quality, operational excellence, and governance
• Modern AI engineering practices that improve developer productivity, deployment velocity, and production reliability
• Strategic AI solutions that drive business innovation, operational efficiency, and customer value across the enterprise
• 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.