AI/LLM Engineer (Saudi Arabia)

This position is for building, deploying and evaluating Client's Arabic AI tutor for mathematics and computer science: preparing Arabic training data, fine-tuning, aligning and evaluating LLMs on Shaheen III and Ibex, and integrating the tutor into the learning platform for a pilot in at least 10 Saudi schools.

The position is part of the RDIA-funded project 5870, “An AI-Powered Arabic Tutoring Platform for STEM Education in the Kingdom.” Two engineers will be hired into this role.

Key Responsibilities

• Prepare Arabic training datasets from educational materials and consented platform interactions.
• Fine-tune, align, and evaluate commercial and open-source LLMs using Shaheen III and Ibex computing platforms.
• Develop Arabic STEM benchmarks, preference-training methods, and student-safety guardrails.
• Integrate AI tutoring capabilities into the learning platform, including Arabic right-to-left interfaces.
• Deploy and optimize models for reliable, low-latency real-time interactions.
• Support school pilot implementation, evaluation, and teacher feedback collection.
• Ensure student data privacy and compliance with Saudi data protection requirements.
• Contribute to research publications, technical reports, and project deliverables.

Qualifications & Experience

• MSc in Computer Science, Artificial Intelligence, or a related field; alternatively, a BSc with at least one year of relevant experience.
• Hands-on experience in LLM fine-tuning, evaluation, and AI application deployment.
• Professional proficiency in Arabic and English.
• Experience with RLHF/DPO, educational technology, or mathematics/programming education is preferred.

Technical Skills

• Advanced Python and PyTorch.
• Hugging Face Transformers, PEFT, and TRL.
• Distributed GPU training using DeepSpeed or FSDP.
• LLM evaluation, reward modeling, and preference optimization.
• Arabic NLP, data pipelines, and OCR.
• Model serving and inference optimization using tools such as vLLM.
• APIs, Docker, Git, CI/CD, and production deployment.