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AI Engineer

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Riyadh, KSA
Full-time
Mid-Senior
Onsite
Discovered 3 weeks ago
AI and LLM model developmentMachine learning engineeringArabic NLPDocument classificationComputer vision and OCRAIOps
Free

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Key skills for this role

AI and LLM model developmentMachine learning engineeringArabic NLP
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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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