MSc Artificial Intelligence • AI Solutions Engineer
Python • SQL • AI Agents • Forward Deployed Engineering
I am an AI Solutions Engineer at FORM IT Solutions, where I lead the company's AI capability as its sole AI specialist. I design, build and deploy production AI solutions for small and medium-sized enterprises, including LLM agents, retrieval-augmented generation (RAG) systems and agent-driven web applications, delivered primarily on a Microsoft Azure and Foundry.
My interests centre on applying AI to complex, real-world problems, particularly those involving structured data, forecasting, optimisation and decision-making under uncertainty. I am most drawn to forward deployed engineering roles, where I can work directly with customers to understand their operations, design solutions around their data and take them through to production.
I combine strong analytical and statistical thinking with a practical understanding of how AI systems behave once they are deployed. I value clarity, reproducibility and technical precision, and I am able to communicate complex technical concepts to technical and non-technical stakeholders alike, a skill central to scoping and delivering client work.
Combining Deep learning with Explainable AI (XAI) for the prediction of Cardiovascular Disease. Implemented using PyTorch
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Implemented reinforcement learning approaches to train a sensor-equipped F1-Tenth car to race on a track. Extended the baseline with a Human-in-the-Loop approach and compared performance through evaluation.
Preprocessed an unfiltered raw dataset using EDA and outlier-resistant normalisation. Built a DNN in Python using Keras to predict house prices and compared results against a traditional neural network using metrics such as RMSE.
MSc Artificial Intelligence — Merit (69%)
Northumbria University, 2024 – 2025
BSc Computer Science — 2:1 (65%)
Newcastle University, 2021 – 2024
Advanced Machine Learning on Google Cloud
Professional certification
Languages: Python, SQL, Java, JavaScript, HTML, C, C++
AI: LLM agents, retrieval-augemented generation (RAG), model selection
ML: PyTorch, scikit-learn, evaluation/validation, XAI
Data: pandas, NumPy, data cleaning, feature engineering, end-to-end pipelines
Engineering: Git, APIs, OOP, cloud deployment
Interests: Agentic AI, Finance, Machine Learning, Statistics