Azure AI Cloud Engineer
Summary
Azure AI Cloud Engineer responsible for designing, implementing, and optimizing scalable Azure cloud infrastructure using Infrastructure-as-Code (Terraform) and supporting AI/ML workloads. Daily work involves building automated deployment pipelines, managing Azure AI platform components, and collaborating with MLOps teams in a hybrid setting.
Introduction & Summary
The Azure AI Cloud Engineer will be tasked with designing, implementing, and optimizing scalable and resilient cloud infrastructure on Azure platforms. The ideal candidate must possess profound knowledge of Azure services and architecture, hands-on experience in Infrastructure-as-Code (IaC) using Terraform, along with an enthusiasm for developing AI/ML workloads and automation practices.
Main Responsibilities
- Design and implement secure, scalable, and highly available cloud infrastructure utilizing Azure services.
- Develop automated deployment pipelines through Infrastructure-as-Code (IaC) tools.
- Implement and manage security practices to ensure data protection.
- Design and manage Azure AI and Data platform components for AI exploration and deployment.
- Collaborate with teams in AI and MLOps to establish governed AI environments.
- Leverage AI tools to enhance engineering productivity.
- Maintain comprehensive documentation of infrastructure architecture.
- Collaborate closely with the enterprise architect and DevOps managers.
- Stay updated with GCP/Azure developments and recommend improvements.
Key Requirements
- Bachelor's degree in information technology, Computer Science, or related field;
- Minimum of 5 years of experience in cloud engineering or architecture;
- Proven experience with GCP/Azure services;
- Hands-on experience with Infrastructure-as-Code tools like Terraform;
- Strong scripting skills in Python, Bash, or PowerShell;
- Familiarity with CI/CD tools;
- Knowledge of networking fundamentals and secure connectivity;
- Proficiency in monitoring and logging tools;
- Understanding of cybersecurity principles and best practices;
- Preferably with relevant cloud certifications.
Nice to Have
- Experience with agentic AI systems or coding assistants;
- Hands-on use of AI tools for operational automation;
- Contextual knowledge of AI, data, and MLOps space.
Other Details
This hybrid role mandates 3 days per week in the office, located in a metropolitan area. Ensure availability to work within the CET time zone and be prepared for a hands-on, engaging project environment.