Lead DevOps Engineer - AI & Data (Foundation Models)

Posted Yesterday
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Dundrum, Tipperary, IRL
Hybrid
Senior level
Blockchain • Fintech • Payments • Consulting • Cryptocurrency • Cybersecurity • Quantum Computing
We are a global technology company in the payments industry.
The Role
Design, build, and operate scalable cloud infrastructure and CI/CD pipelines to deploy and run foundation models and data workloads. Automate deployments, maintain Databricks integrations, ensure observability, security, and high availability, and collaborate with AI, data, and software engineers to streamline production delivery and incident response.
Summary Generated by Built In
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 DevOps Engineer - AI & Data (Foundation Models)
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
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.

Skills Required

  • 5-8 years experience in DevOps, platform engineering, or similar supporting production systems
  • Experience building and maintaining CI/CD pipelines in production environments
  • Hands-on experience with cloud platforms, particularly AWS
  • Experience with Azure or GCP
  • Experience with infrastructure-as-code tools (Terraform, CloudFormation, or similar)
  • Familiarity with containerization and orchestration (Docker, Kubernetes)
  • Experience supporting data and AI workloads, including model deployment pipelines, batch processing, or streaming jobs
  • Working knowledge of monitoring and observability tools (logging, metrics, tracing)
  • Experience with Databricks or similar data platforms
  • Strong understanding of software delivery practices, version control, and automated testing
  • Familiarity with security best practices in cloud and CI/CD environments (secrets management, IAM, least privilege)
  • Strong problem-solving skills and effective communication/collaboration with engineering and data teams

What the Team is Saying

Jenny
Mastercard

Mastercard Compensation & Benefits Highlights

  • Retirement Support A 10% company retirement match (401k or equivalent) is explicitly highlighted in company materials. This level of employer contribution stands out as a core strength of the package.
  • Leave & Time Off Breadth A global minimum of 16 weeks fully paid new‑parent leave and generous U.S. PTO (vacation, personal days, holidays, sick time, and bereavement) are clearly spelled out. These provisions indicate broad time‑off coverage across life events.
  • Wellbeing & Lifestyle Benefits Hybrid work, a four‑week “work from elsewhere” option, meeting‑free well‑being days, five paid volunteer days, mental‑health resources, and fitness reimbursement/on‑site gyms are emphasized. Together they reflect a holistic approach to flexibility and wellbeing.

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The Company
HQ: Purchase, NY
38,800 Employees
Year Founded: 1966

What We Do

Mastercard powers economies and empowers people in 200+ countries and territories worldwide. Together with our customers, we’re building a resilient 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.

Why Work With Us

We live the Mastercard Way: creating value in the communities we touch, growing together through the opportunities we see, and moving fast to innovate and scale. Our collaborative culture and our passionate people are the key to what we do, driving meaningful change as one team and connecting everyone to priceless possibilities.

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Hybrid Workspace

Employees engage in a combination of remote and on-site work.

In our ongoing workplace evolution, we’ve introduced hybrid work, Work-From-Elsewhere Weeks and Meeting-Free Days.

Typical time on-site: 3 days a week
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