Senior ML Ops Engineer - AI Production & Governance

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

Job summary

Mastercard is seeking a hands-on ML/AI platform engineer in Ireland to design and operate end-to-end ML pipelines, from training to production

including experiment tracking, model registries, and secure release processes. The role emphasizes observability, governance, and cost-aware workload orchestration on Databricks with strong collaboration across engineering, data, and security teams.

Qualifications

• Experience with ML model deployment pipelines and MLOps practices.
• Familiarity with cloud security and CI/CD security controls.
• Experience with Databricks or a comparable platform for orchestration.
• Ability to design scalable AI/ML data pipelines and governance.

Responsibilities

• Build and operate pipelines, deployment workflows, and production-readiness practices that turn trained models into reliable, governed services.
• Own experiment tracking and model registry practices using MLflow (or equivalent).
• Implement drift and model-performance monitoring to detect data drift and degradation.
• Design safe model release and rollout with canary/shadow patterns and rollback procedures.
• Orchestrate training and inference workloads on Databricks and manage recurring jobs.
• Develop observability: logging, metrics, tracing with SLIs/SLOs for real-time and batch workloads.
• Set up automated evaluation gates for offline metrics and monitor costs for GPU workloads.
• Design and build CI/CD pipelines for AI/data workloads and enforce security standards.

Skills

Problem solving

Cross-functional collaboration

Architecture & design

Security best practices

Tools

AWS

Databricks

MLflow

Terraform

Docker

Kubernetes

Job description

Mastercard is seeking a hands-on ML/AI platform engineer in Ireland to design and operate end-to-end ML pipelines, from training to production

including experiment tracking, model registries, and secure release processes. The role emphasizes observability, governance, and cost-aware workload orchestration on Databricks with strong collaboration across engineering, data, and security teams.