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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 Engineer Overview As a Lead AI Engineer within Mastercard's AI Center of Excellence, you will provide technical leadership for the design, architecture, and delivery of enterprise AI and Agentic AI solutions that support Mastercard's strategic initiatives. You will lead multidisciplinary engineering teams in building scalable, secure, and production-ready AI platforms and applications while driving engineering excellence, innovation, and adoption of AI across the organization You will own the technical vision for AI engineering initiatives, establish enterprise standards and best practices, and collaborate closely with product, architecture, security, infrastructure, and business stakeholders to deliver high-impact AI capabilities at scale Key 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 Required 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 - Experience architecting and deploying Agentic AI applications using LLMs, RAG, vector databases, AI agents, and model evaluation frameworks - Strong understanding of AI governance, Responsible AI, security, privacy, and regulatory compliance - Excellent communication, stakeholder management, and technical leadership skills What You'll Deliver - 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 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.
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.
