Lead Site Reliability Engineer (AI/ML)

MasterCard; Dublin; On-site; EUR 130,000 - 190,000; Full time

Job summary

Mastercard Ireland is seeking a Lead Site Reliability Engineer (AI/ML) to drive the end-to-end deployment and operation of AI/ML models, ensuring they are scalable, reliable, and integrated into core business processes.

You will establish robust monitoring, partner with AI engineers, risk, and governance teams, and lead incident response, root cause analysis, and continual improvements across production AI systems.

Qualifications

• Bachelor's degree in Computer Science, Engineering, Data Science, Business, or a related field.
• 8+ years of experience in AI/ML operations, MLOps, DevOps, or a related role with a strong focus on production AI/ML deployments.
• Experience deploying and managing AI/ML solutions in production environments.
• Strong understanding of the AI/ML lifecycle from data prep to monitoring.
• Experience with cloud platforms and their AI/ML services.
• Knowledge of data governance, data quality, and data security principles relevant to AI/ML.

Responsibilities

• Lead the E2E deployment and operationalization of AI/ML models and solutions, ensuring scalability and reliability.
• Establish and maintain robust monitoring frameworks for deployed AI solutions; identify performance bottlenecks and data drifts, driving resolution.
• Collaborate with stakeholders, AI engineers, and product teams to define success metrics and ensure AI deployments support business objectives.
• Implement and champion MLOps best practices, automation, and efficient workflows across the deployment lifecycle.
• Coordinate with risk, compliance, and governance teams to adhere to internal policies and regulatory requirements.
• Lead incident response for AI models, perform root cause analysis, and implement preventative measures.

Skills

AI/ML lifecycle

Cloud platforms

Scripting

Containerization

CI/CD for ML

Monitoring tools

Data governance & security

Stakeholder communication

Cross-functional leadership

Operational excellence

Education

Bachelor's degree in CS/Engineering/Data Science/Business

Tools

Kubernetes

CI/CD pipelines for ML

Cloud AI/ML services

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 Site Reliability Engineer (AI/ML)

Key Responsibilities

• Lead the E2E deployment and operationalization of AI/ML models and solutions, ensuring they are scalable, reliable, and integrated seamlessly into existing business processes
• Establish and maintain robust monitoring frameworks for deployed AI solutions. Proactively identify performance bottlenecks, data drifts, and other issues, and drive their resolution to ensure optimal business outcomes
• Work closely with business stakeholders, AI Engineers, and product teams to understand business requirements, define success metrics for AI solutions, and ensure deployed models are directly contributing to key business objectives
• Implement and champion MLOps best practices, automation strategies, and efficient workflows to streamline the deployment lifecycle of AI models, from experimentation to production
• Collaborate with risk, compliance, and governance teams to ensure all AI deployments adhere to internal policies, regulatory requirements, and ethical AI principles
• Lead the response to operational incidents related to deployed AI models, conducting root cause analysis and implementing preventative measures

Qualifications

• Education: Bachelor's degree in Computer Science, Engineering, Data Science, Business, or a related field
• Experience: Minimum of 8+ years of experience in AI/ML operations, MLOps, DevOps, or a related role with a strong focus on deploying and managing AI/ML solutions in production environments.
  • Technical Skills:
    • Solid understanding of the AI/ML lifecycle, from data preparation and model training to deployment and monitoring.
    • Experience with one of the cloud platforms and their AI/ML services
    • Proficiency in scripting and
    • Familiarity with containerization technologies
    • Knowledge of CI/CD pipelines for machine learning models.
    • Experience with monitoring tools for AI/ML solutions
    • Understanding of data governance, data quality, and data security principles relevant to AI/ML
  • Strong ability to understand business needs, translate them into technical requirements for AI solutions, and articulate the business value of AI deployments
  • Excellent communication, interpersonal, and stakeholder management skills
  • Ability to effectively bridge the gap between technical and business teams
  • Demonstrated ability to lead initiatives, drive cross-functional projects, and influence outcomes without direct authority
  • Strong understanding of operational processes and a passion for optimizing them

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.

Everyone wants easier ways to pay; we invent them. Checkout lines are slow; we speed them along. Merchants want more sales; we give them data and insights. People need financial access; we connect them. Corporate purchasing is complicated; we make it simple. Commuters are busy; we speed them on their way. Governments need greater efficiencies; we help create them. Small businesses are virtual; we give them access to a world of buyers. Retailers want to fight fraud; we provide the tools.