AI Platform Engineering Lead

Mastercard Inc.; Atlanta (GA); On-site; USD 140,000 - 231,000; Full time

Job summary

Mastercard Inc. is seeking a Lead AI Platform Engineer to lead the architecture, design, and operation of enterprise AI platforms. You will shape capabilities spanning cloud, MLOps, governance, and security to enable teams to build and deploy AI solutions at scale.

You will mentor engineers, drive observability, reliability, and governance, and partner with product and security teams to translate complex requirements into scalable platforms across public and private clouds.

Qualifications

• Experience designing, building, and operating cloud-native platforms or large-scale distributed systems in enterprise environments.
• Strong experience delivering solutions across public and private cloud environments.
• Experience deploying and managing containerized workloads using Kubernetes or OpenShift.
• Advanced software engineering and automation experience using Python.
• Experience leading the technical design and delivery of complex initiatives involving multiple teams or stakeholders.
• Experience implementing CI/CD pipelines, Infrastructure as Code, automated testing, and modern DevOps practices.
• Experience designing or supporting production AI, machine learning, data platforms, or distributed systems.
• Strong understanding of MLOps, model lifecycle management, production inference, and AI system operations.
• Experience implementing telemetry, monitoring, logging, alerting, and operational analytics.
• Ability to diagnose complex issues, lead root cause analysis, and implement sustainable corrective actions.
• Ability to assess architectural options, explain technical tradeoffs, and influence cross-functional decisions.
• Ability to communicate effectively with technical and non-technical stakeholders.
• Experience guiding, mentoring, or reviewing the work of other engineers.

Responsibilities

• Lead the design, development, and operation of enterprise platforms supporting machine learning, generative AI, agentic AI, and advanced analytics workloads.
• Translate business, engineering, and operational requirements into scalable platform solutions and implementation approaches.
• Design and maintain advanced AI and machine learning systems that meet enterprise requirements for security, reliability, availability, scalability, and performance.
• Own technical delivery across public and private cloud environments, identifying dependencies, resolving complex challenges, and influencing cross-functional decisions.
• Build and optimize services and workflows that simplify onboarding, development, deployment, operations, and lifecycle management for AI solutions.
• Develop infrastructure, tooling, and services supporting model training, evaluation, fine-tuning, deployment, inference, monitoring, and governance.
• Lead the implementation of repeatable, scalable, and secure MLOps capabilities and AI development workflows.
• Build and optimize data ingestion, preprocessing, and feature engineering workflows supporting model training and inference.
• Design observability capabilities including telemetry, performance monitoring, logging, alerting, operational analytics, model monitoring, and drift detection.
• Ensure the operational stability and scalability of AI platforms, driving improvements to reliability, resilience, efficiency, security, and customer experience.
• Lead troubleshooting, root cause analysis, and incident response for complex platform issues and translate lessons learned into sustainable improvements.
• Influence platform architecture through design discussions, reviews, prototypes, tradeoff analysis, and technical recommendations.
• Partner with engineering and product teams to understand requirements, guide solution design, enable adoption, and accelerate delivery of AI-powered products.
• Collaborate with infrastructure, security, architecture, risk, and governance teams to align capabilities with enterprise standards, controls, ethical guidelines, and regulatory requirements.
• Evaluate emerging AI technologies and industry trends and recommend improvements to Mastercard's AI ecosystem and roadmap.
• Drive engineering best practices across Infrastructure as Code, CI/CD, automated testing, secure development, documentation, and operational excellence.
• Guide and mentor engineers through design guidance, technical reviews, problem-solving support, and knowledge sharing.

Skills

Cloud platforms

Kubernetes/OpenShift

Python

CI/CD

MLOps

Observability

AI/ML platforms

Leadership

Communication

Mentoring

Architectural judgment

Tools

OpenShift

Kubernetes

Docker

Helm

GitOps

Infrastructure as Code

Job description

Mastercard Inc. is seeking a Lead AI Platform Engineer to lead the architecture, design, and operation of enterprise AI platforms. You will shape capabilities spanning cloud, MLOps, governance, and security to enable teams to build and deploy AI solutions at scale.

You will mentor engineers, drive observability, reliability, and governance, and partner with product and security teams to translate complex requirements into scalable platforms across public and private clouds.