Job 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. 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; 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). Benefits 5 years ML/AI engineering, in production LLM deployment with knowledge in Python, PyTorch, Hugging Face Kubernetes in production; GPU-served inference GCP Vertex AI or any equivellent cloud
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