Staff ML Engineer & AI Research Scientist · Reading, UK
I build and research LLM systems: agentic AI, retrieval-augmented generation, LLM post-training and evaluation, and multimodal learning. At Method (Hitachi Digital) I lead end-to-end AI solution development, building AI systems that capture organisational knowledge and turn it into actionable AI capabilities.
Before that I was a Research Scientist at Huawei Noah's Ark Lab, working on LLM reasoning with reinforcement learning and agentic workflows, and a Postdoctoral Research Associate at Queen Mary University of London, working on longitudinal NLP. I hold a PhD in Artificial Intelligence from The Open University (Marie Curie scholarship) on quantum-inspired multimodal representation learning and have published at ACL, EMNLP, AAAI and IJCAI.
AI Under the Hood Study notes on ML foundations, why models fail to generalise, how they are optimised, how generative models and transformers work, how training scales across GPUs, how it all comes together in LLMs, how to deploy them, and how agents use tools. Read →Full list on Google Scholar.