I build AI systems, lead the teams that deliver them, and translate between the people who build the technology and the people who need to trust it. More than 30 years across healthcare technology, FinTech, applied ML/AI strategy, data architecture, and enterprise delivery in regulated environments.
Technical architecture, organizational delivery, and stakeholder alignment in regulated environments. I have led engineering organizations, stood up operating models, navigated model risk governance, and shipped production ML tools across regulated financial institutions. I have also sat across from C-Suite and risk officers to explain why the work matters in language that drives decisions.
My career began in healthcare, providing holmium and lithotripsy lasers to surgeons in operating rooms, building one of the earliest web-enabled mobile CT imaging platforms before cloud computing existed, and working alongside hospital operations before transitioning into enterprise technology and leadership. Over the next 25 years I led AI, machine learning, engineering, and technology strategy in highly regulated industries including the Federal Reserve and Capital One before founding Carter Brinkley Consulting.
I build systems with architectural discipline, evaluation rigor, and awareness of what regulated environments demand. My development approach is AI-assisted. I drive architecture and design decisions, AI accelerates the implementation, and I own the understanding. Every system built includes evaluation from Day 1, not as an afterthought.
Most engineering leaders are credible in the engineering room. Business consultants are credible with company stakeholders. Governance professionals understand policy. I'm across all three.
Technology vision, AI/ML roadmaps, and strategic planning for executive leadership. Organizational operating models for AI adoption and advancement in regulated industries.
Data and application integration audits, data flow mapping, and architectural assessments with current-state findings and future-state recommendations.
PMO scaffolding, program strategy, portfolio management, and metrics-driven tracking for technology organizations. From discovery through delivery.
Production-patterned AI systems: retrieval pipelines, document intelligence, multi-agent orchestration, AI governance as architecture, and provider-agnostic model integration.
Five public repositories demonstrating hands-on AI/ML engineering alongside leadership experience. All systems include evaluation from Day 1, no framework dependency, and AI governance as a first-class architectural concern.
Based in Richmond, VA. Available for consulting engagements, advisory work, and leadership opportunities in AI/ML engineering.