AI/ML Platform SRE Lead — Scale & Operate Models

Mastercard; Lusk; On-site; EUR 120,000 - 160,000; Full time

Job summary

Mastercard leads the deployment of AI/ML models in enterprise environments, focusing on scalable production systems, monitoring, and continuous improvement. The Lead Site Reliability Engineer will translate AI models from development to production, ensuring measurable business value and reliable performance.

The role emphasizes collaboration with AI Engineers, product teams, and governance to maintain robust pipelines, security, and operational excellence across all AI initiatives.

Qualifications

• Bachelor’s degree in Computer Science, Engineering, Data Science, Business, or a related field.
• 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.

Responsibilities

• Lead the end-to-end deployment and operationalization of AI/ML models and solutions, ensuring they are scalable, reliable, and integrated into business processes.
• Establish and maintain robust monitoring frameworks for deployed AI solutions and drive resolution of performance bottlenecks and data drifts.
• Collaborate with stakeholders, AI engineers, and product teams to define success metrics and ensure models contribute to business objectives.
• Implement and champion MLOps best practices, automation strategies, and efficient deployment workflows from experimentation to production.
• Work with risk, compliance, and governance teams to adhere to internal policies and ethical AI principles.
• Lead incident response for AI deployments with root cause analysis and preventative actions.

Skills

AI/ML lifecycle

MLOps

Cloud platforms

Scripting

Containerization

CI/CD for ML

Monitoring tools

Data governance

Education

Bachelor’s degree in CS/Engineering/Data Science

Tools

Docker

Kubernetes

Job description

Mastercard leads the deployment of AI/ML models in enterprise environments, focusing on scalable production systems, monitoring, and continuous improvement. The Lead Site Reliability Engineer will translate AI models from development to production, ensuring measurable business value and reliable performance.

The role emphasizes collaboration with AI Engineers, product teams, and governance to maintain robust pipelines, security, and operational excellence across all AI initiatives.