Jorge Gallego Feliciano

Jorge is a skilled LLM engineer with a strong foundation in mathematics and advanced computing. He specializes in designing and implementing AI-driven solutions, including generating synthetic datasets at scale and fine-tuning large language models using state-of-the-art distributed training techniques.

At Aimpoint Digital Labs, Jorge plays a key role in research collaborations with NYU, focusing on improving the efficiency of LLM pre-training through statistical methods. His work involves leveraging clusters of cutting-edge GPUs to pre-train models with up to 1.5 billion parameters from scratch.

Jorge earned his MSc in Advanced Computing (with Distinction) from Imperial College London, where he specialized in Language AI. His thesis explored augmenting LLMs through the integration of external tools. Since then, he has contributed to AI research and engineering in healthcare, as well as platform engineering for large-scale machine learning operations within the London startup ecosystem.

He has hands-on experience deploying proprietary models and training in-house models with up to 405 billion parameters, demonstrating both technical depth and practical expertise in large-scale AI systems.