Description

You will manage and automate the end-to-end machine learning lifecycle within Azure environments, ensuring scalable and reliable production systems.

This role is on-site.

Responsibilities

• Automate ML pipelines, including CI/CD integration and model lifecycle management.
• Monitor and troubleshoot production ML environments to ensure zero downtime.
• Optimize cloud infrastructure and implement cost-saving strategies.
• Deploy scalable ML solutions using containerization and microservices architecture.
• Collaborate with cross-functional teams to support AI research initiatives.

Required Skills

• 5+ years of experience with Azure Machine Learning Service (AMLS) and Kubernetes (AKS).
• Proficiency in Python for machine learning and automation.
• Hands-on experience with Azure DevOps, Git, and CI/CD pipelines.
• Strong knowledge of PowerShell for scripting and automation.
• Experience with Spark and PySpark for data processing.
• Familiarity with Azure Synapse and Azure Data Factory.
• Practical knowledge of containerization and microservices.
• Understanding of resource optimization and cloud-native deployment.
• Knowledge of compliance requirements such as Safe Secret.

Preferred Skills

• Experience with GPT model integration.

Key Skills

Education

Any Graduate