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Lead DevOps Engineer – AI & Data (Foundation Models)

Mastercard -
Dublin, County Dublin

Apply Now

Job details

Full-time
2 days ago

Qualifications

CI/CD
Azure
Kubernetes
DevOps
Google Cloud Platform
AWS
Docker
Continuous improvement
Terraform
AI

Full job description

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.

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data engineering team lead databricks jobs in killester, d

Lead DevOps Engineer – AI & Data (Foundation Models)

Customer & Consumer Data Product Owner

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