About Our Client
- Our client is a rapidly growing AI technology company headquartered in Abu Dhabi, specializing in Enterprise AI, Generative AI, and Large Language Model (LLM) solutions.
- Working closely with government organizations and leading enterprises across the Middle East, the company develops secure, scalable, and sovereign AI capabilities, with a strong emphasis on multilingual and Arabic-language AI applications.
Responsibilities
- Design and execute LLM fine-tuning and post-training workflows using SFT, LoRA, QLoRA, DPO, and RLHF
- Build and improve high-quality training datasets, including data preparation, annotation standards, synthetic data generation, and quality assurance
- Develop robust model evaluation frameworks using automated benchmarks and human-in-the-loop assessment methods
- Enhance model performance through distributed training, quantization, and inference optimization
- Deploy customized LLM solutions in private-cloud, on-premise, and security-sensitive environments
- Partner with product, solutions, and engineering teams to deliver enterprise AI use cases
- Evaluate emerging foundation models and recommend optimal architectures for business requirements
- Contribute to MLOps best practices, experiment tracking, and reproducible training pipelines
Requirements
- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, or a related field
- 3+ years of Machine Learning Engineering experience with hands-on exposure to LLM training, fine-tuning, or model optimization
- Strong proficiency in Python, PyTorch, and the Hugging Face ecosystem
- Practical experience with LoRA, QLoRA, PEFT, DPO, RLHF, ORPO, or similar techniques
- Solid understanding of Transformer architectures, tokenization, context management, and LLM inference
- Experience training models on multi-GPU infrastructure using frameworks such as DeepSpeed or FSDP
- Strong analytical skills with experience measuring and improving model quality
- Excellent communication skills in English
