Applied AI,
made practical.

Following a question beyond the obvious answer. My research explores how models learn useful representations of complex behaviour.

Researched self-supervised representations using graph embeddings, contrastive learning and non-Euclidean geometry.

Used the autonomy of my role to investigate emerging methods and experiment with their application to practical problems.

Multimodal contrastive learning is an area of study, not a claim of an implemented or deployed multimodal system.

• PyTorch
• Multimodal contrastive learning (study)

Taking an idea from architecture to an end-to-end demonstration, and making its value clear to the people deciding what comes next.

Designed an agentic solution for organisation-wide use, with persistent state, audit trails and mandatory human-review gates.

Built an end-to-end working demonstration. Presented the core concept, value and scaling approach to senior stakeholders.

Retained to develop a funded internal pilot. Intended scope is not a claim of organisation-wide deployment.

• Python
• Agent orchestration
• Human review

Working with large-scale financial data, then connecting analysis, modelling and evaluation to a concrete problem.

Analysed financial transaction data with Python, SQL and Snowflake, and completed two fraud-model optimisations during placement.

Applied feature engineering, hyperparameter tuning and performance evaluation.

Gained experience in GPU compute and batch training; no public throughput or model-performance benchmark is claimed.

• SQL
• GPU compute
• Batch training