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AI and ML Cloud Infrastructure Engineer

Deploy, manage and optimise AI and ML platforms on Azure and AWS, ensuring the scalability, performance and security of AI workloads.

  • Full time
  • Bengaluru, India · On-site
  • $40K–55K / year
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We are looking for an engineer with 5+ years of experience administering AI platforms on Azure and AWS. You will manage, optimise and secure AI and ML platforms across both clouds: configuring and maintaining AI services, managing performance and cost, and supporting AI and ML teams as they deploy, scale and monitor workloads.

The ideal candidate has a strong understanding of cloud-native AI tools, infrastructure as code and DevOps practices.

Responsibilities

AI and ML platform deployment and management

  • Deploy, configure and maintain platforms such as Azure Machine Learning, Azure Cognitive Services, AWS SageMaker, Rekognition and Comprehend.
  • Ensure AI and ML services integrate cleanly with cloud infrastructure.

Cloud infrastructure and compute

  • Manage compute resources such as EC2, EKS, Azure VMs and AKS for AI workloads, balancing cost and scale.
  • Optimise containerised AI workloads using Kubernetes, Docker and serverless architectures.

Storage and data management

  • Oversee storage for AI applications using AWS S3, Azure Blob Storage and data lake architectures.
  • Implement efficient data pipelines that improve data accessibility and performance.

Deployment and support

  • Support data scientists, ML engineers and developers in deploying, monitoring and troubleshooting workloads.
  • Streamline MLOps processes for continuous integration and deployment of AI models.

Security, compliance and collaboration

  • Work with DevOps and security teams to align AI infrastructure with best practice and compliance standards.
  • Implement IAM policies, data encryption and secure access controls for AI workloads.

Performance monitoring and optimisation

  • Monitor AI workloads with AWS CloudWatch, Azure Monitor and other observability tools to optimise cost, latency and throughput.
  • Implement auto-scaling to handle dynamic AI workloads efficiently.

Preferred qualifications

  • Experience deploying AI and ML workloads on Azure and AWS.
  • Strong knowledge of cloud computing, Kubernetes and container orchestration.
  • Hands-on expertise in MLOps, automation and CI/CD for AI applications.
  • Familiarity with infrastructure-as-code tools such as Terraform, CloudFormation or ARM templates.
  • Experience with AI model lifecycle management, monitoring and governance.

Why join us

  • Work on cutting-edge AI and ML projects with a dynamic, innovative team.
  • Build AI solutions that solve real business problems.
  • Competitive salary, career growth and learning opportunities in a fast-moving field.

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