Lead AI/ML Ops Engineer, Foundry RnD

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/ML Ops Engineer, Foundry RnD

Job Description Summary
Overview

We are seeking a highly skilled Lead MLOps Engineer to join the Mastercard Foundry R&D DevOps team. The preferred candidate will have a strong DevOps/ML background, as well as significant experience in automation, Infrastructure as Code (IaC), operational efficiency, and self-service platform automation.

As a Lead MLOps Engineer, you will be responsible for building and scaling AI/ML infrastructure (platform, config and workflow) to support research and development efforts with a focus on Developer Ex. You will also collaborate closely with ML engineers, software teams, and platform engineers to design and deliver high-impact solutions that enhance automation, observability, and security in ML workflows.

Must have
Cloud Expertise – Strong understanding of cloud platforms (Azure/AWS) and AI/ML components such as Databricks, Azure Cognitive Services, and MLflow.
Infrastructure as Code (IaC) – Hands-on experience with Terraform, and IaC orchestration tools like Terragrunt.
Scripting & Automation – Strong command-line proficiency with Bash/Python, or equivalent scripting languages.
Containerisation & Orchestration – Expertise in Kubernetes/Docker and how they optimise ML development workflows.
Monitoring & Observability – Experience with monitoring for ML-specific use cases.
Collaboration & Communication – Excellent written and verbal communication skills, with the ability to work in collaborative, multi-cultural teams.

Nice to have
ML Workflow Automation – Experience in ML pipeline orchestration, using tools such as Jenkins, GitHub Actions, or dedicated compute environments.
Model & Data Management – Familiarity with model registries, AI Agents, Retrieval-Augmented Generation (RAG) techniques, and frameworks like LangChain/LlamaIndex.
Hands-on experience with Databricks, Azure ML, or SageMaker.
Understanding of security best practices for MLOps, including data privacy & compliance in cloud platforms.
Knowledge of ML frameworks like TensorFlow, PyTorch, or Scikit-learn.
Experience working in complex enterprise environments with strict security and compliance requirements.
Strong networking fundamentals, including configuring and maintaining secure mTLS-based communication between services.
Excellent problem-solving skills and attention to detail.
Exposure to Java or R (optional but beneficial for enterprise AI environments).
Hands-on experience with stacks such as Prometheus, Grafana, Splunk, ELK and tuning observability for ML-specific use cases.

Role Responsibilities:
Automate & Optimise AI/ML Infrastructure – Enable scalable, repeatable, and secure AI/ML services for research and development (R&D).
Collaborate Across Teams – Work with ML Engineers, DevOps, and Software teams to design robust ML infrastructure and deployment strategies.
Evaluate & Integrate Emerging Technologies – Continuously assess and integrate MLOps best practices to enhance automation, efficiency, and security.
Monitor & Improve ML Operations – Implement proactive monitoring & alerting solutions to improve system performance, reliability, and operational insights.
Perform Research & Proof-of-Concepts (PoCs) – Conduct research and evaluate new technologies to drive innovation and improve AI/ML development and integration cycles.
Contribute to Internal & External Knowledge Sharing – Document findings, best practices, and PoCs to support broader engineering teams.

The Team
The Foundry DevOps is a multidisciplinary global team that unites Platform, Software, Infrastrucutre, ML, Security, and other engineers under a single umbrella. Our mission is to drive automation, rapid iteration, and an exceptional developer experience while also researching and delivering innovations in the DevOps space.

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.