AI Engineer

Job description

Responsible for developing, deploying, and operating AI/LLM models across dual environments - GCP for public-cloud workloads and Humain sovereign cloud for classified data. Build and fine‑tune LLM/ML models for Arabic NLP, document classification, vision/OCR, and AIOps use cases. Run pre‑deployment evaluation with accuracy baselines, regression and safety testing to justify GPU allocation. Optimize inference through quantization, batching, and context sizing based on measured usage. Deploy on Humain GPUaaS using Kubernetes, GPU partitioning on B300 nodes, quotas, and RBAC. Build equivalent workloads on GCP (Vertex AI, GKE) with classification‑based routing. Own the serving stack (vLLM/TGI), model versioning, CI/CD, and monitoring for latency, token count, GPU utilization, and drift

Requirements

• 5+ years of ML/AI engineering experience
• proficiency in Python
• proficiency in PyTorch
• proficiency in Hugging Face
• experience with Kubernetes in production
• experience with GPU‑served inference
• knowledge of GCP Vertex AI or equivalent cloud
• ability to build and fine‑tune LLM/ML models for Arabic NLP, document classification, and vision
• ability to conduct accuracy baselines, regression, and safety testing
• experience optimizing inference (quantization, batching, context sizing)
• experience deploying models on Humain GPUaaS and GCP
• familiarity with ZATCA data sovereignty and SDAIA requirements (AI Ethics, GenAI Guidelines, PDPL)

Skills