Lead the design and operation of Mastercard’s enterprise AI platform, including model serving, inference APIs, feature stores, vector databases, governance, observability, and developer self-service. Manage and mentor AI engineers, establish MLOps and LLMOps standards, drive cloud-native infrastructure and reliability, and partner with architecture, security, data, product, and data science teams to productionize scalable, secure AI and Generative AI solutions.
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 orchestration platforms
- Strong understanding of platform engineering principles, DevOps, CI/CD, observability, reliability engineering, and automation
- Experience implementing security, governance, and compliance controls within enterprise technology platforms
- Excellent communication, stakeholder management, and leadership skills
What You'll Deliver
- A secure, scalable, and enterprise-ready AI platform that enables rapid development and deployment of AI and Generative AI solutions
- High-performing AI engineering teams that consistently deliver production-quality solutions using standardized platform capabilities
- Enterprise AI engineering practices that improve developer productivity, operational excellence, governance, and platform reliability
- Strategic platform capabilities that accelerate AI innovation while ensuring security, compliance, scalability, and long-term maintainability across Mastercard
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:
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 orchestration platforms
- Strong understanding of platform engineering principles, DevOps, CI/CD, observability, reliability engineering, and automation
- Experience implementing security, governance, and compliance controls within enterprise technology platforms
- Excellent communication, stakeholder management, and leadership skills
What You'll Deliver
- A secure, scalable, and enterprise-ready AI platform that enables rapid development and deployment of AI and Generative AI solutions
- High-performing AI engineering teams that consistently deliver production-quality solutions using standardized platform capabilities
- Enterprise AI engineering practices that improve developer productivity, operational excellence, governance, and platform reliability
- Strategic platform capabilities that accelerate AI innovation while ensuring security, compliance, scalability, and long-term maintainability across Mastercard
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.
Mastercard Dublin, Dublin, IRL Office
One South County, South County Business Park, Dublin, Dublin, Ireland, D18
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Leads the design and evolution of an enterprise AI platform supporting experimentation through production. Manages and mentors AI engineers, defines the technical roadmap, and develops shared capabilities such as model serving, feature stores, vector databases, AI gateways, and registries. Establishes MLOps and LLMOps standards, cloud-native infrastructure, governance, security, observability, reliability, and automation practices. Partners with architecture, cloud, data, security, product, and data science teams to scale secure, resilient, and cost-effective AI adoption.
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Lead the design and evolution of an enterprise AI platform supporting experimentation through production. Manage and mentor AI engineers, define platform roadmaps, establish MLOps and LLMOps standards, and build services for model serving, inference, feature stores, vector databases, governance, and observability. Design secure cloud-native infrastructure using Kubernetes and infrastructure as code, ensure reliability and scalability, productionize AI solutions, and collaborate with architecture, security, data, product, and science teams.
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Blockchain • Fintech • Payments • Consulting • Cryptocurrency • Cybersecurity • Quantum Computing
Lead the design and evolution of Mastercard’s enterprise AI platform, managing and mentoring AI engineers while defining platform roadmaps, MLOps and LLMOps standards, cloud-native infrastructure, model serving, governance, security, observability, and reliability practices. Partner with architecture, cloud, data, security, product, and data science teams to productionize AI solutions and deliver scalable, secure, reusable capabilities that accelerate enterprise AI adoption.
Top Skills:
Ai Orchestration PlatformsAPIsAWSCi/CdDockerFeature StoresGoogle Cloud PlatformInfrastructure As CodeKubernetesLlmopsLlmsMicroservicesAzureMlopsModel RegistriesModel Serving FrameworksObservabilityPythonRagVector Databases
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