Ai Infrastructure / Gpu Engineer

Qualification: Bachelor's or Master's degree (16 years of education) in Computer Science, Computer/Electrical Engineering or a related field from an HEC-recognized university. Preference would be given to foreign qualified candidates with proven track record. Experience: At least 5 years operating GPU/accelerated compute or ML infrastructure at scale, including production model serving. Skills & Competencies: • GPU/accelerator tooling and accelerated runtimes • Kubernetes and GPU scheduling • Model-serving frameworks (Triton, vLLM, TGI or equivalent) • Linux, containers and infrastructure-as-code • Python/Bash Key Performance Indicators (KPIs): • GPU utilisation and cost per training/inference hour • Model-serving latency and availability