Jorge Gallego Feliciano

Jorge is an LLM Engineer with a strong foundation in mathematics and advanced computing. He excels in designing and implementing AI-driven solutions, from creating synthetic datasets at scale to fine-tuning large language models (LLMs) with state-of-the-art distributed training techniques. He is an integral part of our research at Aimpoint Digital Labs, were he is collaborating with NYU in research focused on improving the efficiency of pre-training LLMs through statistical methods. The practical aspect of this work includes harnessing clusters of SOTA GPUs to pre-train models as large as 1.5B parameters from scratch.

Jorge specialised in Language AI during his MSc in Advanced Computing (Distinction) at Imperial College, with a thesis focused on augmenting LLMs through external tools. Since then, he has contributed to AI engineering and research for healthcare applications and platform engineering for large-scale ML operations in the London startup scene. Jorge has experience both deploying up to 405B parameter models in-house, and utilising proprietary models.