Portfolio

Platform Engineering Leadership

This is the current professional lens for demming.dev: AWS, AI platform engineering, MLOps, Terraform, Kubernetes, data engineering, and production software across JVM, TypeScript, Rust, C++, Python, and Go systems.

I will keep this section deliberately selective. When projects are added, they should show architecture boundaries, operational trade-offs, delivery constraints, and what had to be made observable or safe before the system could be relied on.

Cloud and platform engineering

AWS account and network foundations, Terraform module boundaries, Kubernetes operating models, CI/CD, observability, reliability practices, and platform governance.

AI platforms and MLOps

Data and model lineage, evaluation gates, training and inference paths, release controls, model-serving runtime concerns, and feedback loops between production and experimentation.

Data engineering and production software

Data contracts, batch and streaming interfaces, database boundaries, service integration, and implementation work in Java, Rust, C++, Python, and Go.

What comes next

The AI maturity package is the first current portfolio artifact. The next additions should be a careful CV, selected consulting pages, and project notes that show platform ownership, operating constraints, and engineering trade-offs without leaking employer-specific detail.