Senior MLOps Engineer: AI Deployment & Platform

MasterCard; Dublin; On-site; EUR 90,000 - 130,000; Full time

Job summary

Mastercard is seeking a Senior ML Ops Engineer to build and operate end-to-end pipelines, deployment workflows, and production-readiness practices that turn trained models into reliable, governed services.

You will own experiment tracking, model registry, and drift monitoring while orchestrating training and inference on Databricks. This role emphasizes secure CI/CD, cost discipline, and scalable observability across real-time and batch workloads.

Qualifications

• Experience with ML model lifecycle management and deployment practices.
• Hands-on experience building CI/CD pipelines in production environments.
• Strong knowledge of monitoring and observability practices for latency-sensitive and batch workloads.
• Familiarity with security best practices in cloud and CI/CD environments.
• Experience with Databricks or similar unified data/AI platform for workflow orchestration.
• Hands-on cloud experience (AWS, Azure, GCP) as a consumer of managed services.

Responsibilities

• Own experiment tracking and model registry practices using MLflow or equivalent.
• Implement drift and model-performance monitoring for production ML models.
• Implement safe model release and rollout mechanisms (canary/shadow deployments).
• Orchestrate training and inference workloads on Databricks (Workflows/Jobs).
• Design and implement observability (logging, metrics, tracing) with SLIs/SLOs.
• Set up automated evaluation gates for offline metrics and model performance degradation.
• Track cost and resource utilization for GPU-based workloads.
• Develop CI/CD pipelines for AI/data workloads and embed security best practices.

Skills

MLOps experience

CI/CD pipelines

Observability

Security in cloud/CI-CD

Databricks

MLflow

Terraform

Docker

Kubernetes

Cloud platforms (AWS/Azure/GCP)

Cross-team collaboration

Tools

MLflow

Databricks

Terraform

Docker

Kubernetes

CI/CD tooling

Job description

Mastercard is seeking a Senior ML Ops Engineer to build and operate end-to-end pipelines, deployment workflows, and production-readiness practices that turn trained models into reliable, governed services.

You will own experiment tracking, model registry, and drift monitoring while orchestrating training and inference on Databricks. This role emphasizes secure CI/CD, cost discipline, and scalable observability across real-time and batch workloads.