Mlops Engineer + Azure stack Professional

Role Overview

• Design, develop, and manage machine learning operations (MLOps) workflows on the Azure cloud platform.
• Deploy, monitor, and optimize ML models in production to ensure scalability, reliability, and efficiency.
• Collaborate with data scientists, software engineers, and cloud teams to integrate ML pipelines into business applications.

Key Responsibilities

• Implement end-to-end MLOps pipelines for model training, deployment, monitoring, and versioning.
• Manage ML infrastructure and services using Azure ML, Azure Kubernetes Service (AKS), and other Azure Stack components.
• Automate model training, testing, deployment, and rollback processes to ensure continuous delivery.
• Monitor model performance, detect drift, and retrain models as needed to maintain accuracy.
• Collaborate with data engineering and development teams to integrate ML solutions into applications.
• Ensure compliance with cloud security, governance, and best practices for MLOps workflows.
• Optimize ML workflows for cost, performance, and resource efficiency on Azure Stack.

Required Qualifications

• 3–8 years of experience in MLOps, machine learning, or cloud engineering roles.
• Hands-on experience with Azure Stack, Azure ML, AKS, and related cloud services.
• Knowledge of CI/CD pipelines, containerization (Docker), and orchestration (Kubernetes).
• Strong programming skills in Python and familiarity with ML frameworks (TensorFlow, PyTorch, Scikit-learn).