AI Infrastructure Consultant

Job Title: AI Infrastructure Consultant

Job Type: Permanent

Job Location: Riyadh, Saudi Arabia

Job Summary:

We are seeking a seasoned AI Infrastructure Consultant to lead the design, implementation, and optimization of our high-performance computing environment. This role is critical for bridging the gap between raw hardware capabilities (GPUs) and scalable AI/ML model deployment. You will be responsible for ensuring our infrastructure is robust, cost-effective, and capable of supporting complex machine learning workloads at scale.

Roles and Responsibilities:

1. Architecture & Design
   • Assess AI/ML workload requirements to design end-to-end compute, storage, and networking architectures.
   • Architect specialized GPU clusters (NVIDIA A100/H100 or similar) tailored for training and inference.
   • Define high-speed networking requirements (e.g., InfiniBand, RoCE) and low-latency storage solutions for massive datasets.
2. Containerization & Orchestration
   • Implement and manage Docker containerization for consistent model environments.
   • Deploy and scale AI workloads using Kubernetes (or managed services like EKS/GKE/AKS), ensuring high availability and seamless resource scheduling.
3. MLOps & CI/CD Integration
   • Build and maintain robust CI/CD pipelines specifically for AI models, automating the journey from code to production.
   • Integrate automated testing, versioning for models/data, and deployment strategies (Canary, Blue-Green).
4. Monitoring & Cost Optimization
   • Establish comprehensive monitoring frameworks to track infrastructure utilization and GPU health.
   • Analyze performance bottlenecks and implement strategies to optimize cost-performance, ensuring maximum ROI on expensive compute resources.

Required Qualifications & Skills:

• Total Experience: 10+ years in IT Infrastructure, Systems Engineering, or DevOps.
• AI Specialization: 2-3 years of hands on experience specifically in AI/ML infrastructure.
• GPU Expertise: Proven track record in GPU setup, CUDA configurations, and managing hardware acceleration for deep learning.
• Orchestration: Expert level knowledge of Kubernetes and the CNCF ecosystem.
• Cloud & Hybrid: Proficiency in major cloud providers (AWS/Azure/GCP) and on premise data center environments.
• Soft Skills: Strong consultancy mindset with the ability to translate complex technical requirements into actionable architectural roadmaps.