MLOps Azure DevOps Engineer

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