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
Bridging the gap between stakeholders and technical teams to translate business outcomes into scalable AI architectures. I have been involved in the lifecycle: from framing the initial question to delivering the final technical solution.
Cloud & Infrastructure
Design reliable infrastructure for AI workloads, currently across Azure and Databricks and previously GCP. I am comfortable making infrastructure decisions and using Terraform (or other IaC tools).
