Job Description

Introduction
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.

Must have

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
Responsibilities include:

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
Certifications :

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

Educational Qualifications

Batchlors in Computer Science/Applications/BTech Computer Science/MCA
Primary Skills :

GPU Infrastructure Design & Optimization (NVIDIA A100/H100, AMD MI300) CUDA Programming & NVIDIA DGX Systems Setup Containerization with Docker, Kubernetes, and NVIDIA Device Plugin Distributed AI Training (DDP, FSDP, ZeRO, Mixed Precision) PyTorch, TensorFlow, and Model Optimization using TensorRT High-Performance Networking (InfiniBand, RoCEv2, GPUDirect Storage) AI Model Deployment using Triton Inference Server Data Management for AI Pipelines (Lustre, BeeGFS, Ceph, S3) Infrastructure Performance Benchmarking (MLPerf, NCCL Tests) Experience with LLMs and AI Model Scaling Requirements
Secondary Skills :

Slurm Workload Manager for Scheduling AI Jobs PEFT/LoRA/QLoRA-based Fine-tuning Strategies Open-Source AI Tools - Hugging Face, ONNX, FastAPI for Model Serving Integration with ETL/Data Ingestion Pipelines (Kafka, Spark, Airflow) GPU Memory Optimization - HBM Utilization, GPU Resource Scheduling AI Pipeline Automation using Python, Bash, or Terraform Basic Cloud Infrastructure Knowledge (AWS EC2 GPU Instances, Azure ML, GCP Vertex AI) Monitoring & Logging (Prometheus, Grafana, NVIDIA DCGM, ELK Stack) Hybrid Cloud Setup for AI Workloads CI/CD Pipelines for ML Ops (GitHub Actions, MLflow, Kubeflow Pipelines)
Job Details
Role:


AI Computing Infrastructure Engineer - GPU & High-Performance Computing


Location :


Dubai


Close Date :


18-07-2025


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