London / UK · Evidence-led engineering portfolio
AI/ML Engineer & Computational Scientist
Building reliable AI systems, evidence-grounded RAG products and scientific computing workflows.
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About / 01
I work where machine learning, numerical reasoning and dependable software systems meet.
I am an AI/ML engineer and computational scientist based in London, currently completing an MSc in Applied Computational Science and Engineering at Imperial College London. My work sits between evidence-grounded AI, numerical methods and dependable software delivery.
I am most useful on problems where models must operate inside explicit constraints: local inference, traceable retrieval, reproducible experiments, numerical validity and honest evaluation. I prefer systems that expose their assumptions, fail safely and leave a clear path from claim to evidence.
Across current research and team coursework, I have worked with local RAG, computer vision, deep learning, genetic optimisation and scientific simulation. This portfolio separates completed work from prototypes and ongoing research so that every case study can be assessed on the evidence actually available.
Experience / 02
Current research is shown as work in progress; academic credentials are limited to facts documented in the portfolio specification.
Microsoft-linked MSc Independent Research Project
An in-progress, Microsoft-linked MSc research project building a local, evidence-grounded policy copilot for KYC/AML analysts with cited answers and explicit human-review boundaries.
Read case studyImperial College London
Applied AI, scientific computing, numerical methods and software engineering, including the current independent research project listed above.
University of Nottingham
Undergraduate foundation in computer science and software development; degree classification recorded as First Class.
Capabilities / 03
Capabilities are described through demonstrated project methods, without percentages or unsupported seniority claims.
Local inference, hybrid retrieval, citation verification, abstention and human-review boundaries for policy-facing AI workflows.
Current KYC/AML independent research projectSimulation-aware optimisation, iterative numerical solvers, structural validity checks and controlled experimental comparisons.
Genetic Algorithm Circuit Optimizer courseworkReusable data, preprocessing, metrics, plotting and submission utilities supporting multi-task machine-learning experiments.
Storm Prediction ML team courseworkOpenMP-capable native tooling, seeded runtime comparisons and attention to the cost of validation and diagnostic checks.
C++20 circuit optimisation workflowsTyped boundaries, modular command-line workflows, reproducible tests and explicit separation between targets and measured outcomes.
Portfolio, vision prototype and research evaluation harnessesProjects 01—05
Five evidence-led case studies across local AI, computer vision, scientific machine learning, optimisation and product engineering.
An in-progress, Microsoft-linked MSc research project building a local, evidence-grounded policy copilot for KYC/AML analysts with cited answers and explicit human-review boundaries.
A computer-vision prototype combining calibrated ArUco pose estimation, MediaPipe hand landmarks and a Kalman-based fusion path with real-time visualisation and recordable experiment outputs.
An in-progress product-engineering initiative defining an agent-ready development pack for a campus job radar, with planned specification, backlog and privacy/compliance workflows.
Team coursework exploring four storm-prediction tasks across 800 example storms, supported by a shared Python package for data, metrics, preprocessing, plotting and submission workflows.
A C++20 team coursework system combining circuit simulation, genetic optimisation, structural validity checks and reproducible seeded sweeps with optional robustness extensions.
Contact / 05
I am interested in software engineering, AI/ML, applied research, scientific software and data-intensive engineering roles.
The public CV contains an email address and GitHub profile, with no phone number or private project identifier.