Senior ML Ops Engineer: AI Model Deployment

Mastercard; Bray (OK); On-site; USD 140,000 - 210,000; Full time

Job summary

Mastercard is seeking a Senior Machine Learning Ops - AI Engineering to build and operate pipelines, deployment workflows, and production-readiness practices turning trained models into reliable services. You will own experiment tracking, model registries, and safe release patterns while monitoring drift and performance across real-time and batch workloads.

You will orchestrate Databricks workflows for training/inference, design observability, automate evaluation gates, and enforce security in

Qualifications

• Experience with MLOps tooling: experiment tracking, model registries, and safe deployment patterns.
• Experience supporting AI/ML workloads: deployment pipelines, batch or streaming inference, and training/serving differences.
• Hands-on experience building and maintaining CI/CD pipelines in production.
• Knowledge of monitoring/observability: logs, metrics, tracing for latency-sensitive vs batch workloads.
• Familiarity with cloud security: secrets management, IAM, least-privilege access.
• Experience with Databricks or similar platform: job orchestration and data pipelines.

Responsibilities

• Own experiment tracking and model registry practices: using MLflow (or equivalent).
• Implement drift and model-performance monitoring across data and embeddings.
• Implement safe model release and rollout mechanisms (canary/shadow deployments).
• Orchestrate training and inference workloads on Databricks: jobs/workflows for training cycles and on-demand inference.
• Design observability for AI/ML services: logging, metrics, tracing, SLIs/SLOs.
• Set up automated evaluation gates for offline metrics and performance degradation.
• Track compute costs and resource utilization for GPU training and inference.
• Design and build CI/CD pipelines for AI/data workloads; embed security in pipelines.
• Onboard platform services to centralized infrastructure (APIs, data pipelines).
• Support incident response and post-incident improvements.

Skills

Experiment tracking

Model registries

CI/CD pipelines

Databricks

AWS / cloud platforms

Security practices

Docker

Kubernetes

Tools

MLflow

Job description

Mastercard is seeking a Senior Machine Learning Ops - AI Engineering to build and operate pipelines, deployment workflows, and production-readiness practices turning trained models into reliable services. You will own experiment tracking, model registries, and safe release patterns while monitoring drift and performance across real-time and batch workloads.

You will orchestrate Databricks workflows for training/inference, design observability, automate evaluation gates, and enforce security in