Most of what I work on falls into four areas.
Applied AI & Optimisation
In practice that has meant forecasting and demand modelling, optimisation in supply chains, and retrieval-augmented assistants over internal data.
ML Engineering
Training pipelines, model serving, drift monitoring, retraining, and governance work. I believe that the principles don't change with the stack, for example solid orchestration, automated testing, and observability that catches problems before users do.
Solution Design
Translate business outcomes into data and AI architectures. I sit between stakeholders and the technical team(s) from framing the question, translating it into a technical solution, and delivering on it.
Cloud & Infrastructure
Design reliable infrastructure for AI workloads, currently across Azure and Databricks and previously GCP. I am comfortable making infrastructure decisions and experience with Terraform.
