ABOUT ME

I'm Ryan McVey — Innovative Technology Architect specializing in Cloud, AI, and Hybrid Infrastructure. I design practical, secure, and scalable technology systems that connect strategy to execution. My background spans public sector, private sector, and military environments, with more than 20 years of experience across systems administration, application modernization, cloud architecture, observability, governance, and AI-enabled solution design.

Today, my work centers on hybrid and multi-cloud architecture, SDLC modernization, platform governance, and responsible AI adoption. I focus on helping organizations move beyond one-time migration efforts into durable operating models that improve security, compliance, delivery speed, cost visibility, and long-term resilience.

Rooted in Service

My career began with service in the United States Marine Corps, where I developed a strong foundational understanding of technology principles, disciplined processes, and governance standards. That early experience shaped a career-long commitment to accountability, mission readiness, and structured problem-solving — themes that have carried through the majority of my work in government and technology fields.

Today, service remains a defining part of my family's life. My spouse is an active-duty Navy sailor currently training as a Hospital Corpsman. Commitment to service is a choice — and one I plan to keep choosing.

Core Skills

• Cloud & Infrastructure: Azure, hybrid/multi-cloud architecture, IaC (Bicep, Terraform), serverless, containers
• DevOps & Delivery: CI/CD (GitHub Actions), GitOps, SDLC modernization, platform engineering
• Governance & Security: Policy as code, compliance frameworks, identity management, zero-trust design
• AI & Automation: Responsible AI adoption, Copilot-assisted development, intelligent automation
• Leadership: Technical strategy, cross-functional delivery, stakeholder alignment, mentorship

Philosophy

Beyond Architecture

I am a builder and tinkerer at heart. My interests include CAD, CNC, 3D printing, fabrication workflows, and turning ideas into usable systems and physical prototypes.

This page is inspired by the Cloud Guru Azure Resume Challenge and is being modernized end-to-end using AgentGitOps.

AI-Driven Project Delivery

This resume site is being modernized end-to-end using AgentGitOps — a methodology that combines GitHub Copilot coding agent, GitHub Issues, GitHub Projects, and GitHub Actions workflows to deliver infrastructure and application changes through an AI-assisted, human-supervised pipeline.

How It Works

The approach follows a structured, multi-phase workflow:

• Assessment — An AI agent reads the entire codebase, IaC templates, workflows, and configuration to produce architecture documentation, assessment CLI commands, and a known-issues inventory.
• Backlog Research — The agent generates a phased backlog plan with individual issue definitions stored as markdown files with YAML frontmatter, enabling scripted label extraction and creation.
• Issue Population — Automation scripts create GitHub labels, milestones, and issues via the gh CLI, producing a fully labeled, project-tracked backlog ready for sprint planning.
• Backlog Burn-Down — Issues labeled Copilot: Yes are assigned to the GitHub Copilot coding agent, which creates feature branches, implements changes, and opens pull requests. Copilot: Partial tasks are worked collaboratively in Codespaces with human oversight.
• Phase Retrospectives — At each phase boundary, a retrospective script collects issue, PR, and commit statistics to calculate Human vs. Copilot AI Productivity KPIs, tracking AI leverage at both the task level and commit level.

The Human-Intent-First Approach

AgentGitOps is not simply about placing humans in a review-and-validate loop at the end of an AI workflow. The defining principle is that humans set the intent, goals, and success criteria at the very start of any meaningful effort — before a single agent session begins or a single line of code is written. The AI operates within that human-defined structure, not alongside it after the fact.

Session 0 is the mechanism that makes this concrete: a structured conversation between the project owner and an AI planning agent. The human articulates what they want to achieve, when, and why. The agent translates that into a phased backlog, milestone plan, and issue taxonomy — a shared artifact that every subsequent automated session references. The backlog becomes the contract between human intent and AI execution, and every phase retrospective measures delivery against the goals the human defined at the outset.

This makes AgentGitOps a practical and cost-effective way to experience AI-assisted delivery in action for small to medium efforts — a single developer or small team can run a complete structured delivery cycle with measurable Human vs. AI productivity KPIs. The same modular, phase-based approach scales equally well to large-scale efforts: the structured assessment and scoping process provides a rigorous foundation for estimating complexity, identifying risk, and planning work that would otherwise be difficult to scope accurately before committing resources.

Bootstrap for Any Repository

The AgentGitOps workflow is designed to be repository-agnostic. The same backlog builder pattern can be applied to any new or existing repository for:

1. Application framework upgrades (e.g., .NET Core 3.1 → .NET 8)
2. Cloud service provider migrations (e.g., Azure CDN → Cloudflare)
3. Security hardening and compliance remediation
4. Cost optimization and resource right-sizing
5. Greenfield project scaffolding with structured delivery phases

Getting started takes just three steps: copy the bootstrap/ directory into your repository, open a GitHub Codespace with Copilot in Plan mode (Claude Opus 4.6), and paste in the Session 0 prompt. The agent asks for your project goals, phases, and timeline — then populates all scripts and creates the initial backlog automatically. No manual script editing required.

Applied to This Site

This azure-resume-iac repository uses the AgentGitOps workflow across six phases: Assessment, Fix Function App, Content Update, Dev Deployment, Prod Deployment, and Cleanup & Docs. The project backlog includes 59+ issues with structured labels for phase, priority, size, area, and Copilot suitability — enabling filtered views for sprint planning, roadmap tracking, and an AI task queue.

Key project infrastructure includes:

• Azure Bicep IaC targeting Azure Functions (.NET 8), Cosmos DB, Key Vault, and Storage Accounts
• Cloudflare CDN with WAF for origin protection and geo-restricted access
• GitHub Actions CI/CD with path-filtered workflows for backend, frontend, and infrastructure
• GitHub Projects (V2) with custom fields for Phase, Priority, Size, and Copilot Suitability

Professional Overview Slides

A slide deck summarizing my career highlights, technical expertise, and professional accomplishments.