AI Computing Infrastructure Engineer – GPU & High-Performance Computing

Riyadh, Saudi Arabia

contractual

Company Description

NETS is a leading global Solutions Provider and Systems Integrator dedicated empowering the future through our integrated approach and commitment to delivering Innovative, Intelligent, and Integrated Solutions (NETS 3 I’s) Effectively, Efficiently, and Economically (NETS 3 E’s). Our service portfolio covers 3 verticals namely Infrastructure, Digital, and Managed Solutions, and NETS Services include Access Networks (Fixed and Wireless), Enterprise Data Networks, Cloud Solutions, Cyber Security, Automation, Resource Outsourcing, and Managed Services. NETS brings over 4 decades of proven domain expertise, service specialization, and industry leadership, delivering over 3,000+ successful projects. Our 1,000+ highly skilled & professional staff, collaboration with over 50 leading global technology partners, 100+ NETS OEM Partners, and NETS Reach, with offices in the UK, UAE, USA, Saudi Arabia, and Pakistan, has allowed us to be the preferred trusted partner to over 200 long-standing satisfied customers including fortune 500 companies across 25+ countries.

Job Description

AI Computing Infrastructure Engineer – GPU & High-Performance Computing

Role Overview

We are looking for a highly capable AI Infrastructure Engineer to design, implement, and optimize GPU-accelerated compute environments that power advanced AI and machine learning workloads. This role is critical in building and supporting scalable, high-performance infrastructure across data centers and hybrid cloud platforms, enabling training, fine-tuning, and inference of modern AI models

Key Responsibilities

• AI Infrastructure Design & Deployment with multi-GPU clusters using NVIDIA or AMD platforms.
• Configure GPU environments using CUDA, DGX Systems, and NVIDIA Kubernetes Device Plugin.
• Deploy and manage containerized environments with Docker, Kubernetes, and Slurm.
• AI Model Support & Optimization for training, fine-tuning, and inference pipelines for LLMs and deep learning models.
• Enable distributed training using DDP, FSDP, and ZeRO, with support for mixed precision.
• Tune infrastructure to optimize model performance, throughput, and GPU utilization.
• Design and operate high-bandwidth, low-latency networks using InfiniBand and RoCE v2.
• Integrate GPUDirect Storage and optimize data flow across Lustre, BeeGFS, and Ceph/S3.
• Support fast data ingestion, ETL pipelines, and large-scale data staging.
• Leverage NVIDIA’s AI stack including cuDNN, NCCL, TensorRT, and Triton Inference Server.
  
  

Conduct performance benchmarking with MLPerf and custom test suites

Requirements

Required Skills & Qualifications

• Bachelor’s or Master’s degree in Computer Science, Engineering, or related field.
• 3–6 years of experience in AI/ML infrastructure engineering or high-performance computing (HPC).
• Solid experience with GPU-based systems, container orchestration, and AI/ML frameworks.
• Familiarity with distributed systems, performance tuning, and large-scale deployments.
• Expertise in modern GPU architectures (e.g., NVIDIA A100/H100, AMD MI300), multi-GPU configurations (NVLink, PCIe, HBM), and accelerator scheduling for AI training and inference workloads.
• Good understanding of modern AI model architectures, including LLMs (e.g., GPT, LLaMA), diffusion models, and multimodal encoder-decoder frameworks, with awareness of their compute and scaling requirements.
• Knowledge of leading AI/ML frameworks (e.g., TensorFlow, PyTorch), NVIDIA’s AI stack (CUDA, cuDNN, TensorRT), and open-source tools like Hugging Face, ONNX, and MLPerf for model development and benchmarking.
• Familiarity with AI pipelines for supervised/unsupervised training, fine-tuning (PEFT/LoRA/QLoRA), and batch or real-time inference, with expertise in distributed training, checkpointing, gradient strategies, and mixed precision optimization.
  
  

Preferred Certifications

• NVIDIA Certified Professional – Data Center AI
• Kubernetes Administrator (CKA)
• CCNP or CCIE Data Center
• Cloud Certification (AWS, Azure, or GCP)