Lead DevOps Engineer – AI & Data (Foundation Models)

Job Title:

Lead DevOps Engineer – AI & Data (Foundation Models)

Overview:

Overview
Mastercard is seeking a DevOps Engineer to support a strategic AI engineering team focused on foundation model development and AI use case delivery. This role is responsible for enabling reliable, scalable, and automated deployment of AI systems—ensuring models and supporting services can move efficiently from development to production.
You will work closely with AI engineers, data engineers, and software engineers to build and maintain the CI/CD pipelines, cloud infrastructure, and runtime environments required to support production AI workloads. This role is critical in ensuring that AI solutions are delivered with strong standards for reliability, security, and operational excellence.
Role
In this role, you will be responsible for building and operating the infrastructure and deployment pipelines that enable AI systems at scale.
Key responsibilities include:
Design and maintain CI/CD pipelines for AI and data workloads, supporting model training, testing, and deployment
Automate build, deployment, and release processes across environments, ensuring consistency and reliability
Manage and optimise cloud infrastructure (primarily AWS, with flexibility across cloud platforms) to support AI and data workloads
Support deployment and operation of AI models, including inference services, batch jobs, and data pipelines
Collaborate with data and AI engineers to streamline development-to-production workflows, reducing friction and cycle time
Implement monitoring, logging, and alerting to ensure system observability and rapid issue resolution
Ensure systems are designed for high availability, resilience, and performance
Embed security best practices into deployment pipelines, including secrets management, access control, and secure configuration
Support Databricks environments and workflows where applicable, including job orchestration and integration with data pipelines
Contribute to troubleshooting production issues and participating in incident response and post‑incident improvements
Promote DevOps best practices, including infrastructure as code, automation, and continuous improvement
All About You
5-8 years of experience in a DevOps, platform engineering, or similar role supporting production systems
Strong experience building and maintaining CI/CD pipelines in production environments
Hands‑on experience with cloud platforms, particularly AWS (experience with Azure or GCP also valuable)
Experience with infrastructure as code tools (e.g. Terraform, CloudFormation, or similar)
Familiarity with containerisation and orchestration (e.g. Docker, Kubernetes)
Experience supporting data and AI workloads, including model deployment pipelines, batch processing, or streaming jobs
Working knowledge of monitoring and observability tools (e.g. logging, metrics, tracing)
Experience with Databricks or similar data platforms is a strong plus
Strong understanding of software delivery practices, version control, and automated testing
Familiarity with security best practices in cloud and CI/CD environments (e.g. secrets management, IAM, least privilege access)
Strong problem‑solving skills and ability to work in a collaborative, fast‑moving environment
Clear communicator, able to work effectively with engineering, data, and platform teams

To find salary ranges and other disclosures for US and Europe countries where applicable, visit https://hrportal.ehr.com/mastercard/Home/Compensation/Compensation/more#. In the US, see link for “salary structures”. For more information on benefits, visit People Place and review the tabs for Benefits and for Time Off & Leave.