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Jobs or company...

Azure MLOPS Engineer - San Antonio, TX (4 days onsite, 1 day remote) - C2C/W2 both will work - 10+ years TX local candidates only

  • Full Time, remote
  • Empower Professionals
  • Hybrid(4 days onsite, United States of America
Salary undisclosed

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Dice
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Role: Azure MLOPS Engineer

Location - San Antonio, TX (4 days onsite, 1 day remote)

Contract 12+ Month

Required Skills: Azure Cloud Azure Machine Learning (AzureML) Azure AI Platform Azure DevOps (ADO) Python

Additional Skills: Azure DP-100 Certification ADLS Azure Synapse

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.

Join our cutting-edge Data Science & AI team and contribute to the future of AI/ML technology! In this role, you'll leverage Azure to drive innovation and operational excellence in our AI/ML initiatives.

Here's what you can expect:

Be at the forefront of AI/ML advancements, applying your skills to solve real-world problems.

Collaborate with a talented team of Data Scientists, ML Engineers, and cloud experts.

Work in a fast-paced environment that fosters continuous learning and growth.

Make a significant impact on the company's success through the power of AI/ML.

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.

Thanks
Aayushi
Senior Technical Recruiter/Lead | Empower Professionals

Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.
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