Lead AI platform Engineer (DevOps)

Lead AI platform Engineer (DevOps)

Who is Mastercard?
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 payment 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.
Our decency quotient (DQ) drives our culture and everything we do inside and outside our company. We cultivate an environment where individuals can thrive, collaborate, and contribute to innovations that power the global economy.
Overview
The AI Platform Engineering team is responsible for building, operating, and evolving Mastercard's enterprise AI platforms and capabilities. Our mission is to provide scalable, secure, and reliable AI infrastructure that enables teams across Mastercard to accelerate the development and deployment of AI-powered solutions.
As a Lead AI Engineer, you will help design, implement, and operate the foundational platforms that support AI and machine learning workloads across the enterprise. You will work at the intersection of platform engineering, cloud infrastructure, MLOps, and AI operations to deliver enterprise-grade capabilities that enable teams to safely develop, deploy, observe, and scale AI solutions.
This team functions as an enterprise AI Platform Engineering organization, delivering and operating shared AI capabilities that enable application teams to build AI-powered products at scale across public and private cloud environments.
The Role
As a Lead AI Engineer, AI Platform Engineering, you will contribute to the architecture, engineering, automation, and operational excellence of Mastercard's AI platforms. You will collaborate with software engineers, data scientists, infrastructure teams, security partners, governance organizations, and business stakeholders to build scalable AI services that support a growing portfolio of AI use cases.
The ideal candidate combines strong software engineering and cloud platform expertise with experience supporting AI and machine learning systems in production environments. You are passionate about automation, reliability, customer enablement, operational excellence, and building platforms that empower others to innovate.
Responsibilities
Design, build, and operate enterprise AI platforms supporting machine learning, generative AI, and advanced analytics workloads.
Engineer scalable solutions across public and private cloud environments, ensuring security, reliability, availability, and performance.
Build and automate platform capabilities that simplify onboarding, deployment, operations, and lifecycle management for AI solutions.
Develop and maintain infrastructure, tooling, and services that support model training, evaluation, deployment, monitoring, and governance.
Implement and enhance MLOps capabilities that enable repeatable, scalable, and secure AI development workflows.
Design and maintain observability solutions, including telemetry, performance monitoring, logging, alerting, operational analytics, and drift detection.
Support production AI platforms and services, proactively identifying opportunities to improve reliability, scalability, efficiency, and customer experience.
Partner with internal engineering teams to understand requirements, enable platform adoption, and accelerate delivery of AI-powered products.
Collaborate with infrastructure, security, architecture, and governance teams to ensure alignment with enterprise standards, controls, and regulatory requirements.
Evaluate emerging AI technologies, platform capabilities, and industry trends to help shape the future direction of Mastercard's AI ecosystem.
Drive automation and engineering best practices through Infrastructure as Code, CI/CD, testing, and operational excellence initiatives.
Participate in troubleshooting, root cause analysis, operational support, and incident response activities to maintain highly available platforms.
Contribute to technical design discussions, architecture reviews, and long-term platform strategy.
Mentor peers and share knowledge across engineering teams while contributing to a culture of continuous improvement.
All About You
Required Qualifications
Experience designing, building, and operating cloud-native systems in enterprise environments.
Strong experience working within both public and private cloud environments.
Experience deploying and managing containerized workloads using Kubernetes or OpenShift.
Strong software engineering and automation experience using Python.
Experience implementing CI/CD pipelines and modern DevOps practices.
Experience supporting production AI, machine learning, data platforms, or large-scale distributed systems.
Strong understanding of MLOps principles and machine learning lifecycle management.
Experience implementing monitoring, observability, telemetry, logging, and operational analytics solutions.
Strong troubleshooting, analytical, and problem-solving skills.
Ability to communicate effectively with technical and non-technical stakeholders.
Experience working in highly collaborative, cross-functional engineering environments.
Preferred Qualifications
Experience supporting generative AI platforms and large language model (LLM) workloads.
Experience implementing model evaluation, finetuning, guardrails, and model governance controls.
Experience with model observability, drift detection, telemetry monitoring, and operational analytics.
Experience with AI serving infrastructure and inference platforms.
Experience supporting GPU-based workloads and accelerated computing environments.
Experience with OpenShift, Kubernetes, Docker, Helm, GitOps, and Infrastructure as Code practices.
Experience with enterprise-scale platform engineering, developer enablement, and self-service platform capabilities.
Familiarity with vector databases, retrieval systems, AI gateways, agentic systems, or emerging AI platform technologies.
Experience working within regulated environments requiring strong security, governance, and compliance controls.
Key Capabilities
The successful candidate will demonstrate proficiency in:
Public and Private Cloud
Model Building, Observability, and Scaling
MLOps (Machine Learning Operations)
Drift Detection and Telemetry Data Monitoring
Model Evaluation, Finetuning, and Guardrails
Deep Learning and Neural Networks
Iterative Model Development
Hyperparameter Tuning
Reinforcement Learning and Feedback Loops
Python
What Makes This Role Unique
This role provides the opportunity to influence and operate the foundational AI platforms that enable innovation across Mastercard. Rather than focusing solely on individual AI models or applications, you will help build and scale the enterprise platforms, tooling, operational practices, and cloud infrastructure that support the next generation of AI capabilities across the organization.
You will work on challenging problems involving platform scalability, reliability, observability, governance, automation, and customer enablement while helping shape Mastercard's long-term AI platform strategy.

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.