Qualifications

• 5+ years of experience in DevOps, Cloud Engineering, or ML Engineering
• 3+ years of hands‑on experience in MLOps or operationalizing ML models in production environments

Key Responsibilities

• Architect and implement scalable end-to-end ML pipelines (training, validation, deployment, monitoring)
• Design and maintain CI/CD pipelines for ML workflows using Azure DevOps
• Implement automated model versioning, artifact management, and rollback strategies
• Provision and manage infrastructure using Infrastructure as Code (Terraform, ARM)
• Deploy containerized ML services using Docker and Kubernetes
• Implement monitoring frameworks for model performance, drift detection, and data quality
• Optimize inference performance, scalability, and cost efficiency
• Ensure compliance, governance, and security best practices in cloud ML environments
• Provide technical leadersh...