Rules in. Reliable systems out.

I am an environmental engineer with more than fourteen years of practice in a field where the details decide everything: reading long, exacting rules closely, turning them into calculations and models, and producing work that has to hold up under review.

That work built a habit I now use everywhere. Break a problem into rules. Find where the data disagrees with itself. Build a repeatable process instead of solving the same thing twice. Outside the day job I point that habit at software — Python automation, REST API integrations, data pipelines and self-hosted systems — increasingly with AI models doing the heavy lifting under tight, testable instructions.

The question I care about is not whether a tool is new. It is whether its output can be trusted, and how you would know when it cannot.

• Analytical problem-solving
• Python
• REST APIs & OAuth
• Data & metadata pipelines
• Linux & Docker
• Networking
• AI workflows
• Prompt engineering