A career spent between disciplines.
I started in data analysis and backend systems, moved through data science and consulting, and eventually found myself responsible for the larger machinery around the work: cloud platforms, delivery pipelines, observability, governance and the long tail of production support.
Today, most of my work sits between AI engineering and data platforms. I may be designing a model-and-tool workflow one day, tracing an unreliable pipeline the next, and helping a team make sense of the architecture after that.
Cloud, MLOps and DevOps are part of that work because useful systems must be deployed, watched and owned. Teaching is part of it too; explaining an idea to a classroom or an engineering team is one of the fastest ways to discover whether it actually holds together.