AI Engineer
I build intelligent systems at the intersection of artificial intelligence, mathematics, and physics.
I graduated from the University of Suffolk with a First Class Honours in Computer Science, specialising in Artificial Intelligence.
My academic work has spanned machine learning pipelines for medical imaging, bioinformatics research with MultiplAI, and educational data mining, always looking for the places where theory becomes transformative practice.
Beyond code, I'm captivated by the elegance of mathematics and the fundamental laws of physics, from Maxwell's equations to the Schrödinger equation. These disciplines don't just inform my thinking; they shape how I approach every engineering problem.
Today I work as a Artificial Intelligence Engineer, building LLMOps pipelines, automation systems, and data-driven products. I believe the best technology feels invisible, it just works.
AI, mathematics, and physics aren't separate interests, they're facets of the same pursuit.
Machine learning pipelines, LLMOps, deep learning architectures, and production AI systems. From ResNet medical imaging to mRNA analysis, building models that make a real difference.
Linear algebra, probability theory, optimisation, and statistics form the bedrock of every model I build. Mathematics isn't a tool, it's the language I think in.
From electromagnetism to quantum mechanics, physics trained me to think about systems from first principles. The universe is the ultimate dataset.
Academic research at the intersection of AI, bioinformatics, medical imaging, and data science.
Designing a machine learning pipeline for mRNA analysis, developing computational approaches for understanding mRNA structures.
ML pipeline for Glioma Analysis using DNA sequencing data, exploring unsupervised learning methods for medical data analysis.
Technical analysis of medical diagnosis using deep learning with ResNet-18 architecture for brain tumour classification from MRI scans.
Clustering algorithms to analyse NASA's meteorite dataset, identifying patterns within extraterrestrial material samples.
Analysis of the Open University Learning Analytics Dataset to predict student withdrawal patterns from engagement metrics.
Comprehensive review of AI approaches to facial recognition, exploring methodologies, ethical considerations, and future directions.
Building AI-powered automation systems, LLMOps pipelines, and data-driven products. Managing AWS infrastructure, Python backends, and full-stack web development across multiple brands.
Hands-on experience in telecommunications technology, network architecture, and enterprise-level IT infrastructure alongside industry professionals.
Promoted awareness of the nuclear industry through the application of physics principles, engaging diverse audiences on nuclear energy and its scientific foundations.
Selected projects spanning machine learning, deep learning, data science and retro computing.
Collaborative research with MultiplAI & University of Suffolk, A machine-learning pipeline for clustering high-dimensional DNA sequencing data to uncover genomic patterns in glioma research.
View on GitHubA high-performance Rust inference gateway that sits in front of LLM backends to provide OpenAI-compatible APIs, intelligent request scheduling, rate limiting, caching, and end-to-end observability.
View on GitHubPredictive modelling of student withdrawal based on virtual learning environment engagement metrics.
View on GitHubA convolutional neural network-based system for classifying brain tumour types from MRI images, showcasing end-to-end medical image analysis.
View on GitHubA video game my grandfather created in the 80's in BASIC on the Vic-20. He named it "Fruit Shop", we are re-programming it by his memory. The set-up is a Vic-20, a Commodore Dataset, A joystick and a very special 78 year old man.
View on GitHubWhen I'm not building AI systems, you'll find me exploring the world. Skiing, travelling, and soaking in new cultures.
Whether it's a research collaboration, a project idea, or just a conversation about physics — I'd love to hear from you.