Azure / AWS Admin / Devops Engineer

Azure/AWS Cloud Administrator (AI Deployment & MLOps)

📍 Remote / Hybrid | Contract / Contract-to-Hire

About the Role

We are seeking an experienced Azure/AWS Cloud Administrator with strong expertise in cloud infrastructure, CI/CD automation, and AI/ML deployment. The ideal candidate will have hands-on experience managing enterprise cloud environments, implementing Infrastructure as Code (IaC), and operationalizing machine learning models in production. This role will work closely with cloud engineers, DevOps teams, and data scientists to deliver scalable, secure, and cost-effective AI solutions.

Key Responsibilities

• Manage, administer, and optimize Azure and/or AWS cloud environments.
• Design, implement, and maintain cloud infrastructure using Infrastructure as Code (Terraform, CloudFormation, ARM/Bicep).
• Build and support CI/CD pipelines using Azure DevOps, GitHub Actions, Jenkins, or AWS CodePipeline.
• Deploy, monitor, and maintain AI/ML models in production using Azure Machine Learning, AWS SageMaker, or similar platforms.
• Manage containerized workloads utilizing Docker and Kubernetes (AKS/EKS).
• Implement cloud security best practices including IAM, networking, compliance, and governance.
• Monitor application, infrastructure, and AI model performance to ensure scalability and reliability.
• Troubleshoot cloud infrastructure, deployment pipelines, and production AI workloads.
• Optimize cloud resource utilization and cost management for AI/ML environments.
• Collaborate with Data Science, DevOps, and Engineering teams to streamline model deployment and operationalization.
• Support model lifecycle management, monitoring, retraining, and deployment automation.

Required Qualifications

• 8+ years of experience administering Azure and/or AWS cloud environments.
• Strong experience building and maintaining CI/CD pipelines using Azure DevOps, GitHub Actions, Jenkins, or AWS CodePipeline.
• Hands-on experience with Terraform, CloudFormation, ARM Templates, or Bicep.
• Proven experience deploying and supporting AI/ML models in production environments.
• Strong knowledge of Docker and Kubernetes (AKS, EKS).
• Proficiency in scripting using Python, Bash, or PowerShell.
• Experience with Azure Machine Learning, AWS SageMaker, or equivalent AI/ML platforms.
• Strong understanding of cloud networking, IAM, security, compliance, and governance.
• Experience implementing monitoring, logging, and observability solutions.
• Ability to troubleshoot complex cloud and deployment issues in production environments.

Preferred Qualifications

• Experience with MLOps frameworks and model lifecycle management.
• Knowledge of TensorFlow, PyTorch, Scikit-learn, or other ML frameworks.
• Experience with AI model evaluation, tuning, and deployment pipelines.
• Familiarity with Azure OpenAI, AWS Bedrock, Generative AI, or LLM deployment.
• Cloud certifications such as:
• AWS Solutions Architect Associate/Professional
• AWS DevOps Engineer
• Microsoft Azure Administrator (AZ-104)
• Azure DevOps Engineer (AZ-400)
• Azure AI Engineer (AI-102)

Nice to Have

• Experience deploying Generative AI, RAG, or AI Agent solutions.
• Knowledge of Databricks, MLflow, Vector Databases, or Kubernetes-based AI platforms.
• Experience supporting enterprise-scale AI initiatives and cloud modernization programs.