AI Engineer

Role Overview

We are seeking an AI Engineer responsible for developing, deploying, and managing AI and large language models (LLMs) in a dual environment encompassing Google Cloud Platform (GCP) for public cloud tasks and a specialized sovereign cloud for handling classified data.

Key Responsibilities

• Construct and fine-tune machine learning and LLM models for Arabic natural language processing, document classification, computer vision/OCR, and artificial intelligence operations.
• Conduct thorough pre-deployment evaluations including accuracy baseline assessments, regression tests, safety checks, and provide justifications for GPU resource allocation.
• Improve inference performance through techniques such as quantization, task batching, and context window sizing based on usage metrics.
• Deploy AI workloads on the sovereign cloud GPU-as-a-service platform—utilizing Kubernetes, GPU partitioning on B300 nodes, resource quotas, and role-based access control mechanisms.
• Create analogous workloads on Google Cloud Platform leveraging Vertex AI and GKE with classification-based routing strategies.
• Manage the serving infrastructure including vector LLMs (vLLM), text generation inference (TGI), model version control, CI/CD pipelines, and monitoring key parameters such as latency, token usage, GPU utilization, and model drift.
• Ensure all AI models comply with data sovereignty laws and AI governance policies including ZATCA, Saudi Data & AI Authority (SDAIA) ethics standards, generative AI guidelines, and personal data protection regulations.

Candidate Requirements

• Minimum of five years of experience in machine learning or AI engineering with practical exposure to production deployment of large language models.
• Proficiency in Python programming, PyTorch framework, and Hugging Face libraries.
• Hands-on experience managing production Kubernetes clusters and GPU-accelerated inference services.
• Familiarity with GCP Vertex AI or equivalent cloud AI platforms.