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The client is seeking an experienced engineer to design, build, configure, and optimise GPU?accelerated infrastructure that underpins their GPUaaS platform across dev, test, and production environments.
Job Responsibility:
Design, implement, and maintain scalable GPU?optimised server clusters
Configure GPU server instances and tune GPU/CPU/memory resources for maximum performance
Utilise cloud platforms and IaC tooling (Kubernetes, Terraform, AWS/Azure) to deploy GPU?accelerated workloads
Implement robust security configurations, access controls, and encryption for GPU and cloud environments
Develop and maintain backup and recovery strategies for GPUaaS
Monitor system performance, identify bottlenecks, and drive continuous optimisation
Collaborate with architects, service management, support teams, and customers on GPU?related features and best?practice performance guidance
Conduct testing, validation, and release support for GPU?driven services
Requirements:
3–5 years in IT engineering or IT operations within cloud?focused environments
Hands?on experience with GPU infrastructure, GPU?optimisation, or AI/ML accelerator technologies
Strong experience with Kubernetes and IaC tools (Terraform or cloud equivalents)
Deep understanding of cloud platforms supporting GPU workloads
Relevant certifications (e.g., NVIDIA CUDA, AI Enterprise, NEMO, Triton) are highly desirable
Nice to have:
Experience with ML frameworks (TensorFlow, PyTorch)
Knowledge of GPU hardware tuning and performance optimisation
Exposure to GPU serverless architectures
Certifications from Intel, AMD, Google, or Cerebras