Description

You will bridge the gap between machine learning development and scalable production systems.

Responsibilities

• Build and maintain scalable CI/CD pipelines for training, testing, deploying, and monitoring models.
• Automate model versioning, deployment, and rollback strategies across staging and production environments.
• Provision and manage ML infrastructure in the cloud using Infrastructure as Code.
• Implement observability, including monitoring, logging, and alerting for model drift and data anomalies.
• Collaborate with Data Scientists and ML Engineers to productionize models and optimize training and inference systems.

Required Skills

• 3+ years of experience in MLOps, DevOps, or ML Engineering.
• Bachelor’s degree in Computer Science, Engineering, or a related field.
• Hands-on experience with Azure DevOps and AzureML.
• Proficiency in Python, Bash, and PowerShell.
• Experience with cloud platforms including AWS, Azure, and GCP.
• Proficiency with containerization technologies such as Docker and Kubernetes.
• Experience with Infrastructure as Code using Terraform or ARM.
• Experience with REST APIs and workflow orchestration using Apache Airflow or Prefect.
• Familiarity with ML frameworks (TensorFlow, PyTorch, or Scikit-learn) and tools (MLflow, TFX, DVC, or Kubeflow).

Preferred Skills

• AZ-400 DevOps Engineer or Certified Kubernetes Administrator (CKA) certification.
• Experience with monitoring solutions like Azure Monitor, Prometheus, or Grafana.

Key Skills

Education

Bachelor's degree