AI Engineer
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Key skills for this role
Role Overview
The AI Engineer will develop, deploy, and operate AI and LLM models across public-cloud and sovereign-cloud environments.
The described environments include GCP for public-cloud workloads and Humain sovereign cloud for classified data.
Key Skills for This Role
Full Job Posting
Role Overview
The AI Engineer will develop, deploy, and operate AI and LLM models across public-cloud and sovereign-cloud environments.
The described environments include GCP for public-cloud workloads and Humain sovereign cloud for classified data.
Key Responsibilities
- Build and fine-tune LLM and ML models for Arabic NLP, document classification, vision/OCR, and AIOps use cases.
- Run pre-deployment accuracy, regression, and safety evaluations and provide evidence to justify GPU allocation.
- Optimize inference through quantization, batching, and context sizing against measured usage.
- 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 the serving stack, including vLLM or TGI, model versioning, CI/CD, and monitoring.
- Monitor latency, token usage, GPU utilization, and model drift.
- Ensure developed AI models comply with ZATCA data sovereignty and SDAIA requirements, including AI ethics, GenAI guidelines, and PDPL.
Required Qualifications
- At least 5 years of ML or AI engineering experience.
- Production experience deploying LLMs.
- Knowledge of Python, PyTorch, and Hugging Face.
- Production Kubernetes experience with GPU-served inference.
- Experience with GCP Vertex AI or an equivalent cloud platform.
Technical Domains
- The role covers Arabic NLP, document classification, computer vision and OCR, and AIOps.
- The serving and infrastructure stack includes Kubernetes, GPU inference, vLLM or TGI, model versioning, CI/CD, and monitoring.
Cloud Environments
- GCP is used for public-cloud workloads.
- Humain sovereign cloud is used for classified data.
- The source does not specify whether the role is remote, hybrid, onsite, or field-based.
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