Seeking a highly talented and experience individual to supervise a core team in the high performance computing environment to manage the server, data storage, software and IT infrastructure in collaboration with our prestigious customers

Key Responsibilities: 

• Plan and execute HPC and AI application performance tuning and optimization activities in accordance with agreed project schedules and objectives.
• Engage with customers and end users to analyse, parallelize and optimize HPC and AI applications, improving performance, scalability and efficient use of computing resources.
• Profile and analyse HPC and AI workloads to identify performance bottlenecks across CPU, GPU, memory, I/O and inter-node communication, and recommend appropriate optimization approaches.
• Optimize GPU-accelerated and distributed AI workloads, including multi-GPU and multi-node workloads using technologies such as CUDA, PyTorch and distributed training frameworks.
• Conduct application benchmarking and scalability analysis to establish performance baselines, evaluate optimization improvements and support efficient utilisation of HPC resources.
• Serve as a subject matter expert in HPC and AI application optimization, applying strong technical expertise and industry knowledge to develop optimized solutions that support customer requirements and business objectives.

Job Requirements:

• Solid understanding of HPC and AI architectures, including CPU/GPU-based computing, parallel computing and distributed workloads.
• Minimum 4 years of relevant experience in application development, performance optimization and numerical model parallelization.
• Strong programming and parallel computing skills in Fortran, C, C++, Python, MPI and OpenMP.
• Strong working knowledge of UNIX/Linux operating systems and HPC application environments.
• Knowledge and experience with numerical simulation applications, such as climate research, weather forecasting, computational science, engineering or aeronautics simulation.
• Proficient in using performance analysis, profiling, compiler analysis and debugging tools, such as Linaro Forge, TotalView, NVIDIA Nsight Systems/Nsight Compute, or equivalent tools.
• Knowledge of GPU computing and CUDA, including profiling and optimization of GPU-accelerated applications.
• Experience with AI/ML frameworks such as PyTorch and/or TensorFlow and running AI workloads in GPU-based computing environments.
• Knowledge of distributed AI training and multi-GPU/multi-node workloads, including technologies such as PyTorch Distributed and Distributed Data Parallel (DDP).
• Strong communication and presentation skills, with the ability to develop and deliver technical workshops, training and knowledge-sharing sessions to HPC and AI users.

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