MLOps Engineer

Role & responsibilities

• Design and implement enterprise-scale MLOps platforms on Azure Cloud.
• Build and manage ML model deployment architectures using Azure Kubernetes Service (AKS).
• Develop CI/CD and CT (Continuous Training) pipelines for ML workflows.
• Automate model packaging, deployment, retraining, and rollback strategies.
• Integrate Azure Machine Learning, AKS, Azure DevOps, GitHub Actions, and container technologies.
• Collaborate with Data Scientists, Data Engineers, Solution Architects, and DevOps teams.
• Implement model monitoring, drift detection, performance tracking, and alerting mechanisms.
• Ensure platform security, compliance, and governance standards.
• Optimize AKS clusters for performance, scalability, and cost efficiency.
• Establish MLOps best practices, reusable frameworks, and deployment standards.