Enterprise AI Platform Lead & MLOps

Mastercard

Donabate

On-site

EUR 120,000 - 160,000

Full time

4 days ago

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Job summary

Mastercard is seeking a Lead AI Platform Engineer to shape and scale an enterprise AI platform that enables teams to build, deploy, and operate AI and Generative AI solutions at scale. You will lead a team of AI engineers, define the platform roadmap, and collaborate across Architecture, Cloud, Data, Security, Product, and Data Science to deliver secure, reusable services and standards.

You will drive platform reliability, governance, and automation while partnering with multiple domains to

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.

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.