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 mentorship to junior engineers Collaborate closely with Data Science and Engineering teams to define production standards Required Skills Strong experience with Microsoft Azure (required) Experience with AWS or GCP (plus) Advanced knowledge of Docker Strong hands‑on experience with Kubernetes (production clusters) Advanced proficiency in Python Experience with Bash and/or PowerShell Experience designing and consuming REST APIs Experience with TensorFlow, PyTorch, or Scikit-learn Familiarity with ML lifecycle tools such as MLflow, Kubeflow, DVC, or TFX Experience with orchestration tools such as Apache Airflow or Prefect Implementation of model drift detection and performance monitoring frameworks Preferred Certifications #J-18808-Ljbffr