Background

Career Overview

These have been the major chapters of my career:

• Egenera (16 years): Documented converged infrastructure systems (unified compute, storage, and networking platforms)
• Pegasystems (9 years): Documented a large-scale platform serving financial services, government, and healthcare
• Legion Intelligence (current): Documenting configuration workflows and system integrations for an AI/ML platform in the government and defense space

Hands-On Documentation Philosophy

Across these roles, one principle has remained constant: I write about systems I use. Before documenting a Kubernetes (container orchestration) configuration on different providers, I deploy an environment after configuring resources for it. I call every API endpoint before I write about it. This hands-on approach catches inaccuracies that theoretical documentation misses. It keeps my writing grounded in the customer and developer perspective.

Why I'm Moving Forward

Enterprise platform documentation served me well for two decades. But the work that energizes me now is different. I'm drawn to companies building the infrastructure powering the next generation of computing:

• Cloud-native platforms: systems designed to run in distributed cloud environments
• MLOps: tools for deploying and managing machine learning models in production
• Observability tools: platforms that provide visibility into system health and performance
• AI gateway and agent infrastructure: platforms that centralize identity-linked access and governance for LLM providers, coding agents, and agentic workflows — where documentation must bridge networking, security, and AI operations audiences simultaneously

I want to help those teams build documentation systems that scale alongside their products, integrate with CI/CD (continuous integration and delivery) pipelines, and serve both human readers and AI agents.

Building for AI-Ready Documentation

I'm investing in a forward-looking specialty: structured knowledge artifacts and llms.txt files. An llms.txt file is a standardized text file placed at a site's root (similar to robots.txt) that helps large language models understand and navigate a product's documentation. As companies increasingly rely on AI to automate developer workflows, documentation needs to be machine-readable from the start.