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