I am a Principal Architect and experienced AI / ML engineer with a background in applied mathematics and the computational sciences. I lead multidisciplinary engineering teams and work with research scientists, engineers, designers and end users to design, build and deploy next generation AI that deliver business value across a wide range of specialised domains including EAM, ERP, ITSM, ITFM, NPM, FSM, AIOps, ITOps, Business Ops, and others.
I have been a member of the British Standards Institute (BSI) ART1 Artificial Intelligence Committee and Ecosystems Architecture and AI working groups at The Open Group contributing to the development of national standards and professional practice for AI. I have previously held roles at the University of London teaching AI and Machine Learning at Masters level. I am certified as an architect and engineer in multiple clouds and have expertise in a wide range of technologies including training / GPU stacks (NVIDIA / Slurm / Ray / PyTorch amongst others) for HPC, HCI, databases, MLOps, DevOps, AI software paradigms, building on self managed infrastructure, cloud-native and hybrid cloud.
My research interests are in novel AI architectures for advanced machine intelligence and quantum mechanics. I graduated with First Class honours in Computer Science and hold a Masters degree from Cambridge University.
Enterprises deploying language models often face the same challenge: how to ensure responses stay on topic, coherent, and aligned with business goals—without drowning in prompt engineering or moderation scripts.
Enterprise AI adoption is accelerating—but so are the risks. From ethical lapses to irrelevant outputs, traditional LLM pipelines struggle with alignment, especially when static rules or prompt engineering are the only lines of defense. What if your AI could learn to stay on-topic, aligned with enterprise values, and semantically coherent—all while adapting over time?
Singular Value Decomposion (SVD) is a well known method for latent semantic analysis. When applied to BERT contextual embeddings SVD produces three components: U, Σ, and V. The eigenvectors in V represent distinct semantic patterns - each one captures a different aspect of meaning in the text. The eigenvalues in Σ tell us how significant each pattern is, effectively showing us what is semantically important and where the semantic “holes” are - the gaps in meaning that separate different semantic clusters. This elegant mathematical decomposition reveals the fundamental building blocks of meaning in text, creating a natural hierarchy of semantic patterns that can be analyzed through linear algebra and externalised a semantic knowledge graph.
Generative AI has high utility for generating metadata for both structured and unstructured data and is relevant in the storage domain where data discoverability drives the value of data across the enterprise including for downstream AI projects.
Operating AI across different clouds and execution engines becomes complex and difficult to maintain with cloud native tools as the number of different integrations between systems proliferates at scale. OpenShiftAI provides a cohesive hybrid, multi-cloud AI platform that enables enterprises to separate concerns between pipeline orchestration and workload execution reducing complexity in the data and governance subdomains and enabling enterprises to operate AI at scale.