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.
Qualifications
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.