About me
My route into AI was not a straight line, and that is the point. I started as a mechanical engineer, spent nine years as a data analyst building failure-prediction models and reporting pipelines for industrial engineering teams, then completed an MSc in Applied Data Science at the University of Sunderland.
That background shapes what I build. I have watched real equipment fail, so my MLOps project predicts machine failure from sensor data. I have worked in compliance-heavy environments, so my RAG system answers questions over regulatory documents with citations, because an unsourced answer is useless in a regulated setting.
Right now I am deepening the production side of the craft through two intensive programmes: a Full Stack Generative AI and Agentic AI Bootcamp and a DevOps, MLOps and LLMOps Engineering Bootcamp. Everything I build is public: the code, the evaluation results and the write-ups.
Projects
Three systems covering the three pillars of modern AI engineering: classical ML with MLOps, retrieval with evaluation, and agents with observability. Each one is a case study, not a tutorial.
Predictive Maintenance MLOps Platform
In progressPredicts industrial equipment failure from sensor data and serves it as a real, monitored API: MLflow experiment tracking, drift detection and automated retraining, informed by a decade of hands-on maintenance engineering.
RAG Compliance & Incident Intelligence Assistant
PlannedGrounded question answering over UK health and safety regulations with citations on every answer, hybrid search with re-ranking, and a RAGAS evaluation harness that runs in CI so retrieval quality is measured, not assumed.
Multi-Agent Security Operations Triage
PlannedCooperating agents that classify, enrich and prioritise incoming incident reports, then draft structured summaries for human approval. Built with the production concerns most agent demos skip: guardrails, cost tracking, observability and trajectory evaluation.
Skills
Languages
- Python (advanced)
- SQL
- Bash
- TypeScript (basic)
Machine Learning
- PyTorch, scikit-learn, XGBoost
- pandas, NumPy
- Feature engineering
- Model evaluation
GenAI & LLMs
- RAG and vector databases
- LangChain, LangGraph
- Prompt engineering, fine-tuning
- LLM evaluation (RAGAS)
MLOps & Backend
- FastAPI, PostgreSQL, Redis
- Docker, GitHub Actions
- MLflow, Evidently
- AWS, Linux
Writing
Technical write-ups from each project: the decisions, the trade-offs and the numbers.
// first post shipping soon
The first write-up covers building the predictive maintenance pipeline: baseline model, MLflow tracking and what the drift monitor caught. Follow along on LinkedIn or watch the GitHub repos.