Senior MLOps Engineer: AI Deployment & Observability

Mastercard; Donabate; On-site; EUR 100,000 - 180,000; Full time

Job summary

Mastercard in Dublin is seeking a Senior Machine Learning Ops - AI Engineering to build and operate pipelines, deployment workflows, and production-readiness practices that turn trained models into reliable, governed services.

You will own experiment tracking, model registries, drift monitoring, and safe release patterns while orchestrating training and inference on Databricks with strong emphasis on security, observability, and scalable CI/CD for AI workloads.

Qualifications

• Experience with MLOps-specific tooling and practices: experiment tracking, model registries, and safe model deployment/rollout patterns (e.g., MLflow or equivalent).
• Experience supporting AI/ML workloads specifically: model deployment pipelines, batch or streaming inference, and the operational differences between training and serving workloads.
• Strong, hands-on experience building and maintaining CI/CD pipelines in production environments, including the judgment to recommend appropriate tools and patterns.
• Working knowledge of monitoring and observability practices: logging, metrics, tracing, and how they apply differently to latency-sensitive versus batch AI workloads.
• Familiarity with security best practices in cloud and CI/CD environments: IAM, least-privilege access, secrets management.
• Experience with Databricks or a similar unified data/AI platform: job orchestration and workflow scheduling.

Responsibilities

• Own experiment tracking and model registry practices up to production governance.
• Implement drift and model-performance monitoring, and rollback procedures for safe releases.
• Orchestrate training and inference workloads on Databricks with scheduled workflows.
• Design observability with logging, metrics, and tracing for both real-time and batch AI workloads.
• Build CI/CD pipelines for AI/ML workloads and embed security into every pipeline.
• Onboard platform services onto centralized infrastructure with API gateways and data pipelines.

Skills

MLOps tooling

Experiment tracking

Model deployment

CI/CD pipelines

Databricks

MLflow

Cloud: AWS

Azure

GCP

Terraform

Docker

Kubernetes

Security best practices

Observability

Python

Tools

Databricks

MLflow

Terraform

Docker

Kubernetes

AWS

Azure

GCP

CI/CD tooling

Job description

Mastercard in Dublin is seeking a Senior Machine Learning Ops - AI Engineering to build and operate pipelines, deployment workflows, and production-readiness practices that turn trained models into reliable, governed services.

You will own experiment tracking, model registries, drift monitoring, and safe release patterns while orchestrating training and inference on Databricks with strong emphasis on security, observability, and scalable CI/CD for AI workloads.