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Urgent! DevOps Engineer – AI Infrastructure Job Opening In Singapore, Singapore – Now Hiring RAYDIAN CLOUD PTE. LTD.

DevOps Engineer – AI Infrastructure



Job description

About the Role

Raydian Cloud is seeking a forward-thinking DevOps Engineer to help build and scale infrastructure that powers cutting-edge AI workloads.

You'll work at the intersection of cloud-native technologies and Artificial Intelligence operations (AIOps), enabling high-performance, secure, and automated environments for AI development and deployment.

Your expertise in Infrastructure as Code and Kubernetes will be critical in supporting scalable AI pipelines and platform services.

Key Responsibilities

  • Design and manage cloud infrastructure optimized for AI/ML workloads using Infrastructure as Code (Terraform, Pulumi, etc.)
  • Deploy and maintain Kubernetes clusters tailored for GPU scheduling, distributed training, and inference workloads
  • Build CI/CD pipelines for AI model training, validation, and deployment across environments
  • Collaborate with data scientists and ML engineers to streamline model lifecycle management
  • Implement observability and monitoring for AI services (e.g., Prometheus, Grafana, OpenTelemetry)
  • Ensure infrastructure security, compliance, and cost-efficiency in multi-tenant AI environments
  • Automate provisioning of AI-specific resources (e.g., GPU nodes, storage volumes, feature stores)
  • Document infrastructure patterns, DevOps workflows, and platform architecture

Required Skills & Qualifications

  • Strong experience with Kubernetes, including GPU scheduling and Helm
  • Proficiency in Infrastructure as Code tools (Terraform, Pulumi, etc.)
  • Familiarity with cloud platforms (AWS, Azure, GCP) and AI services (e.g., SageMaker, Vertex AI)
  • Experience with CI/CD tools (GitHub Actions, GitLab CI, Argo Workflows)
  • Scripting skills in Python, Bash, or Go
  • Understanding of ML model lifecycle and data pipeline orchestration
  • Excellent communication and collaboration skills across technical and business teams

Nice to Have

  • Experience with Kubeflow, MLflow, or similar MLOps frameworks
  • Knowledge of containerized AI workloads (e.g., TensorFlow Serving, Triton Inference Server)
  • Familiarity with service mesh technologies (Istio, Linkerd) in AI microservices
  • Certifications in Kubernetes or cloud platforms (CKA, AWS DevOps Engineer)

Why Join Raydian Cloud?

  • Shape the future of AI infrastructure and platform services
  • Work with a visionary team blending deep tech and strategic execution
  • Influence architecture decisions in a fast-moving AI startup environment
  • Competitive compensation, flexible work culture, and growth opportunities


Required Skill Profession

Other General



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