AI/ML Cloud Engineer

AI/ML Cloud Engineer

Key Responsibilities :

Cloud Infrastructure Management

• Design, deploy, and manage cloud infrastructure supporting AI/ML workloads on AWS and Azure.
• Manage compute resources such as EC2, Azure Virtual Machines, GPU instances, EKS, VPC, ECS, S3, Lambda, Route 53 and Kubernetes clusters.
• Provision and configure storage, networking, and security services for AI platforms.
• Ensure high availability, scalability, and reliability of AI environments.

AI Platform Support

• Deploy and maintain AI/ML services such as:
  • Amazon SageMaker and Azure Microsoft Foundry
  • Azure Machine Learning
  • AI model training and inference environments
• Support data scientists and ML engineers by providing optimized infrastructure for model training and deployment.

Automation & Infrastructure as Code

• Implement Infrastructure as Code (IaC) using tools such as:
  • Terraform
  • CloudFormation
  • ARM templates/Bicep
  • Docker Files
• Automate and set up environment provisioning, patching, and scaling.

Containerization & Orchestration

• Deploy and manage containerized AI workloads using:
  • Docker
  • Kubernetes
  • Amazon EKS
  • Azure Kubernetes Service (AKS)
  • ECS

Monitoring & Performance Optimization

• Monitor system health, performance, and resource utilization using tools like:
  • CloudWatch
  • Azure Monitor
  • Datadog / Prometheus
• Optimize infrastructure for cost, performance, and GPU utilization.

Security & Compliance

• Implement cloud security best practices including:
  • IAM / RBAC management
  • Network security groups
  • Encryption and secrets management
• Ensure compliance with organizational and regulatory standards.

CI/CD & DevOps Integration

• Integrate AI infrastructure with CI/CD pipelines.
• Support automated deployment of models and AI services.

Required Qualifications

• Bachelor's degree in Computer Science, Information Systems, or related field.
• 5+ years experience in infrastructure administration or cloud engineering.
• Strong hands-on experience with AI/ML infrastructure or data platforms.
• Proficiency with Linux administration and scripting (Python, Bash, PowerShell, Terraform, terra grunt).
• Experience with Docker and Kubernetes.
• Experience with GitHub Actions.
• Experience with LLM infrastructure set up.
• Experience with working in centralized team with triaging capabilities.
• AWS cloud services.
• Microsoft Azure cloud services.