AI Infrastructure Engineer

About the Role

Eram Talent seeks a skilled AI Infrastructure Engineer to join their forward-thinking team in Dhahran, Saudi Arabia. The chosen candidate will plan, develop, and uphold scalable, high-performing infrastructure tailored for AI and machine learning workloads. Collaboration with data scientists, ML engineers, and developers is essential to enhance infrastructure efficiency and support seamless AI model development and deployment.

Key Responsibilities

• Architect, deploy, and manage high-performance computing setups specifically designed for AI and ML tasks.
• Operate and sustain GPU-based clusters, cloud AI platforms, and parallel processing frameworks.
• Work closely with data scientists and ML engineers to evaluate infrastructure demands for AI initiatives.
• Maximize resource utilization and scalability to handle large datasets and intricate AI models.
• Automate the provisioning and deployment of infrastructure using Infrastructure as Code methodologies.
• Maintain infrastructure security, compliance, and operational reliability.
• Track system metrics, resolve problems to reduce downtime, and boost productivity.
• Keep abreast of new advancements and best practices in AI infrastructure and suggest continuous enhancements.

Required Qualifications and Experience

• A Bachelor's degree or above in Computer Science, Engineering, or a related technical discipline.
• Over six years of experience in infrastructure engineering, ideally involving AI, machine learning, or high-performance computing environments.
• Expertise in cloud technologies such as GCP, OpenShift, Kubernetes, and Docker containers/images.
• Proficiency in AI workflows including model training, evaluation, and deployment.
• Experience with ML/LLM Operations (ML/LLMOPs).
• Strong understanding of Large Language Models (LLMs) and Generative AI, including their inner workings and inference processes.
• Skills in inference scaling, distributed computing, benchmarking, and planning to meet SLAs and SLOs.
• Knowledge of GPU utilization, distributed workload management, and autoscaling techniques.
• Familiarity with NVIDIA technologies such as NIMs, Superpods (HPC, Slurm, Kubernetes), and the Huggingface ecosystem.
• Ability to design and monitor dashboards for LLM and ML applications.
• Comprehension of AI application architecture and end-to-end system flows.
• Hands-on experience with DevOps tools including CI/CD pipelines, ArgoCD, Git, and Jenkins.
• Programming skills in Python and SQL.