AI Engineer

Job Summary

Develop, deploy, and operate AI/LLM models across dual environments using GCP for public-cloud workloads and Humain sovereign cloud for classified data. Build and optimize models for Arabic NLP, document classification, vision/OCR, and AIOps use cases while ensuring compliance with ZATCA data sovereignty and SDAIA requirements.

Responsibilities

• Build and fine-tune LLM/ML models for Arabic NLP, document classification, vision/OCR, and AIOps applications
• Run pre-deployment evaluation including accuracy baselines, regression testing, and safety testing to justify GPU allocation
• Optimize inference through quantization, batching, and context sizing based on measured usage patterns
• Deploy workloads on Humain GPUaaS using Kubernetes, GPU partitioning on B300 nodes, quotas, and RBAC
• Build equivalent workloads on GCP using Vertex AI and GKE with classification-based routing
• Own serving stack including vLLM/TGI, model versioning, CI/CD, and monitoring for latency, tokens, GPU utilization, and drift
• Ensure developed AI models comply with ZATCA data sovereignty and SDAIA requirements including AI Ethics, GenAI Guidelines, and PDPL

Requirements

• 5 years of ML/AI engineering experience with production LLM deployment
• Proficiency in Python, PyTorch, and Hugging Face
• Production experience with Kubernetes and GPU-served inference
• Experience with GCP Vertex AI or equivalent cloud platform

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