Artificial Intelligence Engineer
Problem-solving • Decision intelligence • Practical systems
I turn ambiguous real-world problems into measurable, working solutions—by combining data, modelling, software engineering, and clear decision-making under uncertainty.
Published work with: Rolls-Royce, DOW, voestalpine, BAT, Equivital, FUCHS, Yili, OCAS, AMRC, and more. See work →
Years in ML/AI
Research + industry
I'm an Artificial Intelligence Engineer focused on practical problem-solving. I enjoy work where the goal is clear impact: improved decisions, reduced risk, faster delivery, and systems that teams can actually run. 9.4 years in ML/AI—from PhD research with Rolls-Royce through to industrial AI delivery across pharma, chemicals, materials, manufacturing, and consumer goods.
My background spans modelling under uncertainty, end-to-end software delivery, and communicating complex ideas to stakeholders. I use whatever approach best fits the problem—from classical methods through to modern ML and LLMs.
Turning messy, complex data into actionable decisions—especially industrial datasets.
Building ML pipelines and backend services (APIs, data flows); end-to-end solution architecture from data to deployed service.
Technical workshops, training, and translating business requirements into delivery roadmaps.
Autonomous agents, multi-agent architectures, and RAG systems—when LLMs are the right tool for the problem.
Building custom proofs-of-concept and demonstrations for enterprise stakeholders.
Speaking at webinars and conferences on AI/ML applications and industrial transformation.
I led these projects from start to finish—framing the problem, wrangling data, building ML models, designing adaptive experiments, and extracting insights through explainable AI. As the primary technical lead, I proved value through pilots that delivered measurable impact on messy industrial data.
ML applications in process safety
Privacy-preserving ML for R&D at AI in Materials conference
ML for Process Safety presentation
How ML + adaptive experimental design can accelerate battery R&D
Metallurgical Optimisation with ML: From Unstructured Data to Structured Insights
Information Alchemy: Extracting Value from Real-World Experimental Data in Materials R&D
I combine problem-framing, modelling, and engineering to deliver solutions that work in the real world.
Clarify what success looks like, the constraints, and the real question to answer.
Prototype quickly to validate the approach before scaling up.
Calibrated predictions and risk-aware evaluation—no wishful assumptions.
From data pipelines to deployed services—built to be used and maintained.
The tools and technologies I work with—from core ML frameworks through to modern LLM systems and industrial domain expertise. Click to expand each category.
PyTorch • TensorFlow • Scikit-Learn • Conformal Prediction • Uncertainty Quantification • Bayesian Optimization • Active Learning • Federated Learning
Python • FastAPI • NumPy • Pandas • SQL • REST APIs
LangChain • LangGraph • RAG Systems • MCP Protocol • Gemini • Claude • OpenAI
Docker • GCP • Vertex AI • Git • CI/CD
Materials Science • Pharmaceuticals • Chemicals • Manufacturing • Consumer Goods • Food & Beverage
Pharma, chemicals, materials science, manufacturing, food & beverage, and aerospace. Most of my work involves high-stakes industrial R&D data.
No—those are tools. I use whatever approach best fits the problem, whether that's classical statistics, ML, or modern LLMs.
PhD in Materials Science with ML (Rolls-Royce sponsored), Chartered Engineer (CEng), 20+ peer-reviewed publications with 290+ citations. Strong in Python, ML/statistics, and backend development.
I start with the decision, not the model. What action will change based on the answer? Then I build the simplest thing that proves value and make it reliable.
Questions about my work? You can reach me via email or LinkedIn.