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About This Opportunity
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 leadership and ...