Leadership scope, grounded in engineering.

1. 01 / Early leadership

   Operational leadership, training, and process

   Air Force

   Early responsibility centered on preparing people and improving how critical work was documented, reported, and carried out.

   • Trained more than 200 personnel
   • Developed processes and operational reporting
   • Handled sensitive information and held responsibility for deployment procedures

2. 02 / Engineering foundation

   Web engineering, experimentation, and systems

   Built an engineering foundation across application development, APIs, experimentation, analytics, and performance.

   • Application architecture and web engineering
   • A/B testing and analytics
   • APIs and performance improvement

3. 03 / Technical leadership

   Mentorship, standards, and platform modernization

   Pocket Network

   Provided principal-level technical leadership through mentorship, coding standards, TDD, CI/CD, SSR, OAuth, and major architectural modernization.

   • Mentored engineers and established coding standards
   • Used TDD and CI/CD to support substantial platform changes
   • Compressed delivery of major modernization work

4. 04 / Enterprise architecture

   Front-End Architect

   Publicis Sapient

   Architected a frontend micro-architecture for a major banking environment and created structures and standards that supported independent team delivery.

   • Enabled dozens of teams to build and deploy independently
   • Mentored engineers across companies and projects; reviewed code across teams
   • Worked on interfaces used by millions of people

5. 05 / Engineering management

   Engineering Manager

   Swiftly

   Managed an approximately seven-developer team supporting multiple products at different SDLC stages. Work crossed engineering management, product ownership, scrum leadership, technical project management, lead development, analysis, and mentoring.

   • Established coding patterns, engineering standards, and deployment/DevOps pipelines
   • Helped create a SaaS approach that reduced deployment/development time from more than six months to under two weeks
   • The described platform work reached greater than 90% test coverage
   • Reported approximately 70% operational cost reduction associated with the re-architecture

6. 06 / Founding engineering

   Founding Engineer

   Collar Networks

   Moved from application architecture into broader product and system architecture, carrying technical decisions across implementation and integration boundaries.

   • Worked across frontend architecture, backend APIs, infrastructure, and automation
   • Integrated marketplace and order-book systems with wallet and Web3 capabilities
   • Connected KYC/AML services, smart-contract interactions, and multiple EVM-compatible networks
   • Provided cross-functional technical leadership across product systems

7. 07 / Current

   Architecture spanning an enterprise AI initiative

   Large enterprise retail environment

   As a contractor, I entered an enterprise AI engagement focused on one product team. My architectural scope now spans capabilities developed by six or more independent engineering teams as they are consolidated toward a unified enterprise AI platform.

   • Application and platform architecture across team boundaries
   • Shared navigation, workflows, service boundaries, and reusable patterns
   • AI developer experience, engineering standards, and organizational enablement

• 01

  Increase engineering leverage

  AI should increase engineering leverage without removing engineering discipline.

• 02

  Prefer deterministic systems first

  Use a small, deterministic tool when it can do the job; reserve model reasoning, including frontier models, for work that benefits from it.

• 03

  Match intelligence to the task

  Use the least expensive model that reliably solves the task, considering latency, privacy, and the cost of failure.

• 04

  Share platform capabilities; keep domain ownership clear

  Centralize reusable AI capabilities while product teams continue to own their domain behavior and data.

• 05

  Measure AI as an engineering system

  Evaluate the workflow around the model: context, tools, validation, review, latency, cost, and outcome.

• 06

  Scale review with risk

  Human review, authorization, and governance should be proportional to the impact of the change.

• 07

  Make each run create evidence

  Capture agent execution and validation so the evidence can improve evaluation, routing, and future systems.

• 08

  Make the preferred path the easiest path

  Good architecture makes reusable, observable, and validated engineering practices easier to follow.

I still build and operate across applications, APIs, data, models, and infrastructure. That firsthand work informs my architecture and leadership decisions.