ML Engineer Azure ML

Key Responsibilities:

• Design and build end-to-end ML pipelines (data processing, training, inference, monitoring) using Azure ML
• Deploy ML/AI/GenAI models using Azure ML Studio
• Manage and deploy models on AKS (Azure Kubernetes Service) clusters
• Implement MLOps best practices including CI/CD pipelines
• Use MLflow for experiment tracking, model registry, and monitoring
• Build and maintain Azure DevOps (ADO) pipelines for automation
• Monitor model performance, drift detection, and reliability in production
• Work with Azure services like Blob Storage, ADF, and DevOps
• Ensure code quality using tools like Linting, Black, and best practices
• Collaborate with cross-functional teams to deliver scalable ML solutions

Qualifications Required:

• Strong Expertise In:

• Azure ML Studio

• Azure Kubernetes Service (AKS)

• MLflow

• Azure DevOps (CI/CD, Git)

• MLOps & Model Lifecycle Management

• Python (Advanced)

• Model Deployment & Monitoring

• Data Pipelines & ML Pipelines Role Overview: As an ML Engineer with expertise in Azure ML, you will be responsible for building, deploying, and managing end-to-end ML/AI/GenAI pipelines on the Azure cloud. Your role will involve designing and implementing scalable machine learning pipelines, managing model lifecycle, and deploying models into production environments within the Azure cloud ecosystem. Working closely with data scientists, data engineers, and DevOps teams, you will contribute to the development of robust, automated, and monitored ML systems. This position requires solid expertise in MLOps, cloud deployment, and productionizing ML models. Key Responsibilities: