About

Profile, experience, and background.

Machine Learning Engineer and PhD researcher working across the full AI stack, from enterprise data and platform architecture to knowledge graphs, model behaviour, and structured reasoning.

I work across both industry and research, designing AI systems for complex real-world environments. My experience spans the full stack, from enterprise data foundations and platform architecture through to semantic systems, model behaviour, and research-led system design.

In industry, I have focused on enterprise-scale data platforms, knowledge graph architectures, MLOps, and intelligent systems for complex operational settings. My work is centred on turning technically ambitious ideas into credible systems that are deployable, reliable, and useful in practice.

My PhD explores how large language models can reason more effectively over structured systems using graph-based representations and world models. More broadly, I’m interested in building machine-understandable representations of complex environments that make AI systems more useful, reliable, and interpretable in practice.

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Experience

Jan 2022 - Present

London / Global

Veolia

Machine Learning Engineer — Global Data Team

Working on enterprise-scale data and AI platform design for complex industrial environments, with a focus on semantic modelling, knowledge graphs, and AI-ready architecture.

Contributed to global data and AI platform design across operational and enterprise systems in a large organisational setting.
Worked on semantic and graph-based approaches for making complex organisational data more consistent and usable.
Collaborated across engineering, data, and business stakeholders on scalable platform direction and data modelling standards.

Enterprise AI Systems

Knowledge Graphs

Semantic Modelling

Data Architecture

Industrial AI

Sept 2024 - Present

London

University College London

PhD Researcher

Researching how large language models can reason more effectively over structured systems using graph-based representations.

Investigating how large language models reason over graph-structured and relational problems.
Explored LLM supervised fine-tuning and architecture changes for improved structured reasoning.
Exploring graph-based representations and world models as a foundation for more capable AI systems.

LLM Reasoning

Graph Representations

Fine-Tuning

World Models

Structured Reasoning

Jan 2021 - Jan 2022

London

Veolia

Data Scientist

Focused on production ML systems, MLOps, industrial data pipelines, and predictive maintenance applications.

Designed and delivered production-grade MLOps workflows for repeatable training, deployment, and monitoring across machine learning use cases.
Helped establish more structured ML delivery patterns across infrastructure, deployment, and observability.
Worked on data platform and pipeline architecture for large-scale industrial data processing and downstream ML applications.
Built API and service layers for model integration and delivery, alongside containerised workflows for production systems.
Delivered predictive maintenance and anomaly detection solutions for industrial data.

MLOps

Production ML

APIs

Containerised Workflows

Predictive Maintenance

Anomaly Detection

Jan 2020 - Jan 2021

London

Veolia

Data Analyst

Built BI, reporting, and data pipeline foundations that later supported broader analytics and machine learning initiatives.

Delivered BI and analytics solutions using Power BI, SQL, and ETL pipelines to support operational decision-making.
Built foundational data pipelines and reporting systems that formed part of later ML and platform initiatives.

SQL

ETL

Reporting

Analytics

Background

Education

Current

PhD — Computer Science (AI)

University College London

Ongoing

Postgraduate

MSc — Computer Science

University of Bath

Distinction (79%)

Undergraduate

BSc — Natural Sciences (Physics / Maths / Chemistry)

University of Bath

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