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.