Azure ML Engineer

Skills

Description

• Proven (3+ years) experience in Azure Cloud engineering with a strong focus on AzureML, including managing ML workflows.

• Expertise in Python for AI/ML workflows, proficient with additional scripting languages.

• Familiar with Azure DevOps (ADO), CI/CD practices, and Azure AI/ML services.

• Strong foundation in AI/ML principles, with practical experience in deploying models at scale.

• Holds a Bachelor’s or master’s degree in computer science, Engineering, or related field, demonstrating a comprehensive understanding of both theoretical and applied aspects of machine learning and software engineering.

• Proven track record of successful AI/ML model deployments in Azure.

• Azure DP-100 Certification preferred (Not mandatory).

Responsibilities

• • Monitor Azure Cloud resources, including AzureML environments and model performance, optimizing resource use and addressing issues to maintain high service reliability.
• • Collaborate with data scientists, Hybrid Cloud, and DevOps for seamless ML model integration into production, ensuring scalability and reliability.
• • Design, implement, and manage ML pipelines within AzureML for end-to-end automation of data processing, model training, validation, and deployment.
• • Utilize AzureML for efficient scaling of ML models, applying best practices in version control, CI/CD, and lifecycle management.
• • Manage CI/CD processes for machine learning models using Azure DevOps tools.
• • Stay updated with the latest in Azure AI/ML features, applying best practices to leverage cloud-based AI/ML technologies effectively.
• • Keep abreast of Azure advancements and best practices to enhance cloud-based AI/ML technology utilization.