Job Tittle : Azure MLOps Engineer
Contract : W2
Duration : 12+ Months
Location : San Antonio, TX
Position Summary
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.
Why Join Us:
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.