GPU / AI Infrastructure Engineer

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.

·       Build and 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 in Infrastructure 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.