MLOps Engineer (W2)

Role

MLOps Engineer (W2) — Remote (Atlanta, GA)

Responsibilities

• Provide L2/L3 production support for AI/ML applications, pipelines, and model deployments
• Monitor model performance, drift, and data quality in production
• Troubleshoot and resolve incidents, alerts, and failures across ML workflows
• Support CI/CD pipelines for model deployment and versioning
• Collaborate with Data Scientists, ML Engineers, and DevOps teams
• Manage model retraining schedules, batch/real-time pipelines, and inference jobs
• Perform root cause analysis (RCA) and implement preventive measures
• Ensure adherence to SLA/SLO targets; maintain operational dashboards/reporting
• Handle application release support, patching, and environment maintenance
• Maintain documentation (runbooks, troubleshooting guides, SOPs)

Requirements

• Strong experience in production/application support for AI/ML systems
• Hands-on Python, SQL, and scripting for troubleshooting
• Knowledge of the ML lifecycle: training, validation, deployment, monitoring
• Cloud experience with Azure/AWS/GCP
• Familiarity with MLOps tools such as MLflow, Kubeflow, SageMaker, Azure ML
• Docker and Kubernetes experience
• CI/CD tooling exposure: Jenkins, GitHub Actions, Azure DevOps
• Monitoring tools: Grafana, Prometheus, ELK, Azure Monitor
• Strong debugging and incident management

Nice to have

• Healthcare/payer domain experience (claims, enrollment, analytics)
• Data engineering tools: Spark, Airflow, Databricks
• Model explainability and governance frameworks
• ITIL process knowledge (incident/problem/change management)

Contract

• 12+ months
• W2 role (rate: $57/hr)