GPU / AI Infrastructure Engineer, Singapur

Job Location:  Singapore (Onsite) Job Summary: We are looking for a GPU / AI Infrastructure Engineer with 5–7 years of experience to build, optimize, and support scalable AI/ML and HPC environments. The ideal candidate will have strong expertise in GPU acceleration, containerized workloads, and MLOps pipelines, along with hands-on experience managing AI infrastructure across on-prem or cloud platforms.Key Responsibilities·       Design, deploy, and manage GPU-enabled infrastructure for AI/ML and HPC workloads. ·       Install, configure, and optimize GPU software stacks including NVIDIA AI Enterprise, CUDA, ROCm, OpenCL, and NIMS. ·       Support GPU acceleration for machine learning frameworks and scientific applications.·       Build and manage containerized environments using Docker, Kubernetes (K8s), and Singularity. ·       Deploy and manage Kubernetes GPU workloads using GPU Operator and related ecosystem tools. ·       Support ML frameworks such as TensorFlow, PyTorch, Scikit-learn, and MXNet.·       Develop and maintain MLOps pipelines using MLflow and Kubeflow. ·       Design and implement Infrastructure as Code (IaC) solutions for AI/ML pipelines. ·       Automate infrastructure provisioning using Terraform, Pulumi, and CloudFormation. ·       Buildand maintain CI/CD pipelines for ML model deployment and infrastructure automation.·       Collaborate with data scientists and engineers to optimize model performance and resource utilization. ·       Monitor GPU utilization, system performance, and troubleshoot issues across the stack. ·       Ensure scalability, reliability, and security of AI infrastructure environments.Required Skills&Qualifications·       5 years of experience in AI/ML infrastructure, HPC, or DevOps engineering roles. ·       Strong experience with GPU technologies and acceleration frameworks (CUDA, ROCm, OpenCL). ·       Hands-on experience with NVIDIA AI Enterprise stack and GPU ecosystem tools (e.g., NIMS, GPU Operator).·       Proficiency in container technologies: Docker, Kubernetes, and Singularity. ·       Experience working with ML frameworks: TensorFlow, PyTorch, Scikit-learn, MXNet. ·       Solid understanding of MLOps tools such as MLflow and Kubeflow. ·       Expertise inInfrastructure as Code (Terraform, Pulumi, CloudFormation).·       Experience building and managing CI/CD pipelines for ML or infrastructure workflows. ·       Strong scripting skills (Python, Bash, or similar). ·       Familiarity with Linux-based environments.