| Azure MLOps Engineer // San Antonio,Texas at Remote, Remote, USA |
| Email: [email protected] |
| Hi , Hope you are doing well, Please find the below JD and share with me some matching profiles. Azure MLOps Engineer Local to San Antonio, TX (Must sit in San Antonio, TX)(4 days onsite, 1 day remote) 12 month contract with strong potential to extend past 24 months Required Skills : Azure Cloud Azure Machine Learning (AzureML) Azure AI Platform Azure DevOps (ADO) Python Additional Skills : Azure DP-100 Certification ADLS Azure Synapse Position Summary We are seeking a highly skilled Azure Cloud Engineer with a strong background in Azure Cloud Infrastructure, Microsoft Intelligent Data Platform (ADLS, Azure Synapse), and Azure Machine Learning (AzureML) to join our Data Science & AI team. This role is critical to our mission of using AI to achieve improved safety, sustainability, and responsibility. The ideal candidate will be responsible for designing, implementing, and maintaining our entire AI/ML environment within Azure, including the machine learning operations infrastructure. You'll ensure efficient model development, deployment, and monitoring processes, working closely with Data Scientists and ML Engineers to guarantee a smooth transition from development to production. Key Responsibilities As an Azure Cloud Engineer on our Data Science & AI team, you will play a critical role in designing, implementing, and maintaining our AI/ML environment within Azure. You'll collaborate closely with Data Scientists, Hybrid Cloud, and DevOps engineers to ensure a seamless transition of ML models from development to production. Here's a breakdown of your key responsibilities: Azure Cloud Management: Manage and maintain the Microsoft Azure cloud infrastructure, services, and solutions relevant to AI/ML operations. Use Microsoft Infrastructure as Code (IaC) and Terraform pipelines for deploying Azure resources. Monitor Azure Cloud resources, including Azure AI services, AzureML environments, and model performance, optimizing resource use and addressing issues to maintain high service reliability. Machine Learning Operations (MLOps): Design, implement, and manage end-to-end ML pipelines within AzureML for data processing, model training, validation, and deployment. Utilize AzureML for efficient scaling of ML models, applying best practices in version control, CI/CD (using Azure DevOps tools), and lifecycle management. Collaboration and Integration: Collaborate with Data Scientists, ML Engineers, Hybrid Cloud, and DevOps engineers to ensure seamless integration of AI/ML models into production, focusing on scalability and reliability. Additional Skills: Stay updated with the latest advancements in Azure AI/ML features and best practices to leverage cloud-based AI/ML technologies effectively. Qualifications To be successful in this role, you will ideally possess the following qualifications: Bachelor's or Master's degree in Computer Science, Engineering, or a related field, demonstrating a comprehensive understanding of both theoretical and applied aspects of cloud engineering/infrastructure. 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 (e.g., Bash, PowerShell). Familiarity 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. Proven track record of successful AI/ML model deployments in Azure. Azure DP-100 Certification preferred. -- Thanks & Regards Viswanath Maremalla Talent Acquisition specialist 1601 N Harrison Ave, STE # 2B, Pierre, SD 57501 Phone: 605-776-2218 EXT 116 |Direct Number: 6057762218 | [email protected] LinkedIn : https://www.linkedin.com/in/viswanath-maremalla/ -- Keywords: continuous integration continuous deployment artificial intelligence machine learning information technology South Dakota Texas Azure MLOps Engineer // San Antonio,Texas [email protected] |
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| Tue Oct 29 23:07:00 UTC 2024 |