An AI hiring platform still ran hiring as a fully manual process, done by the founder.

Built an end-to-end pipeline with the Anthropic SDK: multi-stage agents with self-checking schemas, deterministic decision logic and bias audits at key decision points.

One automated pipeline run replaces the manual process, with bugs fixed across 3 rounds of production test runs.

Engineering knowledge was spread across thousands of technical manuals that reference, replace and build on each other, so one answer could sit across many documents.

Led the project. Built a pipeline that extracts and cleans text from the PDFs, a custom spaCy model that spots document codes, and relationship extraction that labels whether one manual references or supersedes another. Then a RAG assistant on top.

Thousands of linked manuals became one knowledge graph engineers can query, supporting infrastructure asset management.

Most ML work is a config file pointed at someone else's library. I wanted to prove I understood the machinery underneath.

Wrote CUDA kernels by hand (tiled matmul, warp-shuffle softmax, tiled attention) and my own autograd engine, then trained one decoder on text and on MovieLens 1M without changing it.

Tiled attention runs 25.7x faster on a Tesla T4 than my CPU version at sequence length 1024, it scores 98% on MNIST, and it runs live as a movie recommender.

Rain, fog and snow made roadside CCTV hard to read, which weakened road monitoring.

Trained CNN models on 10,000 CCTV images to recognise the weather in each frame, and a model to remove it from the picture.

98% weather classification accuracy, making road monitoring more reliable.

Crash investigators need to know exactly which car was involved, and how safe it is, often from a single photo.

Built a two-branch ResNet50 that predicts make, model and year with calibrated confidence, trained on Stanford Cars (8,144 images, 196 classes), and matched to Euro NCAP safety ratings.

46 ms model inference per image. Make 96.4%, model 91.0%, year 89.1%, and 81.7% with all three right.

£10,000 of discounted shoebox gifts had to reach 50 community groups, and every order went through a spreadsheet.

Designed and built the whole system solo: an online order form, automatic payment and confirmation, and a live stock dashboard with low-stock alerts.

£10k of products and 50 groups handled in one place, replacing the spreadsheet to cut admin time and order errors.