Senior ML Ops Engineer: AI Deployment & Governance

Mastercard; Lusk (WY); On-site; USD 140,000 - 210,000; Full time

Job summary

Mastercard is seeking a Senior ML Ops Engineer to design, deploy, and govern AI/ML services at scale. The role focuses on turning trained models into reliable production services with robust experiment tracking, model registries, and safe deployment patterns.

You will orchestrate Databricks workloads, implement drift monitoring, and build secure CI/CD pipelines for training, evaluation, and serving. Strong cloud, container, and observability experience are essential.

Qualifications

• Experience with MLOps tooling for experiment tracking and model registries.
• Proven ability to deploy and monitor ML models in production.
• Strong CI/CD experience for ML pipelines.

Responsibilities

• Own experiment tracking and model registry practices: using MLflow (or equivalent) to manage model versioning and the lifecycle.
• Implement drift and model-performance monitoring: detecting data drift and degradation.
• Implement safe model release and rollout mechanisms: canary/shadow deployments and rollback procedures.
• Design observability for AI/ML services with logging, metrics, and tracing across real-time and batch workloads.
• Design and build CI/CD pipelines for AI and data workloads, including training, evaluation, and deployment.
• Embed security practices into pipelines: secrets management and least-privilege access.
• Support incident response and post-incident improvements across production systems.

Skills

MLOps tooling

Model deployment pipelines

Databricks / Spark

CI/CD in production

Cloud platforms (AWS, Azure, GCP)

Docker & Kubernetes

Observability & monitoring

Security best practices

Python / ML engineering

Tools

MLflow

Terraform

Databricks

Docker

Kubernetes

CI/CD tools (GitHub Actions, Jenkins)

Job description

Mastercard is seeking a Senior ML Ops Engineer to design, deploy, and govern AI/ML services at scale. The role focuses on turning trained models into reliable production services with robust experiment tracking, model registries, and safe deployment patterns.

You will orchestrate Databricks workloads, implement drift monitoring, and build secure CI/CD pipelines for training, evaluation, and serving. Strong cloud, container, and observability experience are essential.