Requirements
Build and fine-tune LLM/ML models for Arabic NLP, document classification, vision/OCR, and AIOps use cases. Run pre-deployment evaluation, accuracy baselines, regression and safety testing. Optimize inference (quantization, batching, context sizing). Deploy on Humain GPUaaS (Kubernetes, GPU partitioning) and GCP (Vertex AI, GKE). Own serving stack, model versioning, CI/CD, and monitoring. Ensure AI models comply with ZATCA and SDAIA requirements.
Description
Looking for an AI Engineer to Develop, deploy, and operate AI/LLM models across Clinets dual environment — GCP for public-cloud workloads, Humain sovereign cloud for classified data.RequirementsBuild and fine-tune LLM/ML models for Arabic NLP, document classification, vision/OCR, and AIOps use cases. Run pre-deployment evaluationAccuracy baselines, regression and safety testing; evidence to justify GPU allocation.Optimize inference — quantization, batching, context sizing — against measured usage.Deploy on Humain GPUaaS: Kubernetes, GPU partitioning on B300 nodes, quotas, RBAC.Build equivalent workloads on GCP (Vertex AI, GKE) with classification-based routing.Own serving stack (vLLM/TGI), model versioning, CI/CD, and monitoring for latency, tokens, GPU utilization, and drift.Ensuring developed AI Models Complying with ZATCA data sovereignty and SDAIA requirements (AI Ethics, GenAI Guidelines, PDPL).Benefits5 years ML/AI engineering, in production LLM deployment with knowledge inPython, PyTorch, Hugging FaceKubernetes in production; GPU-served inferenceGCP Vertex AI or any equivellent cloud