I believe we're in a critical period where AI can either amplify human intelligence and dignity, or replace and diminish it. The difference lies not in the algorithms we build, but in the problems we choose to solve and how we approach solving them.
My approach starts with genuine human needs - the psychological isolation of astronauts, the workflow inefficiencies that stress healthcare workers, the frustration that drives students away from technical subjects, the security gaps that threaten manufacturing operations. Then I build AI systems that serve these needs while maintaining privacy, ethical standards, and regulatory compliance.
uShip (January 2015 – May 2020) - Built enterprise logistics platform serving 500+ shipping companies with C++ core systems and web interfaces. Led architectural decisions for high-volume freight matching algorithms and real-time tracking systems. This experience taught me that scalable systems require both performance optimization and human-centered design.
Working on logistics optimization problems - route planning, load matching, pricing algorithms - gave me foundational experience in the mathematical and computational thinking that would later inform my AI system architecture.
ICS (Information and Computer Systems, 2020-Present) - Working for an MSP that focuses on law firms opened my eyes to how far behind the legal industry is with automation and modern technology adoption. Observing these systemic inefficiencies while taking software development and LAN cybersecurity classes allowed me to quickly bridge the gap between different roles.
I evolved from client services professional to account executive to engineering team lead (non-technical), serving as a point of contact for office administrators to ensure their system issues get resolved. This unique perspective - understanding both client pain points and technical solutions - became the foundation for my AI engineering approach focused on practical business problems rather than theoretical applications.
The transition from C++ systems programming to AI engineering happened through hands-on experience with enterprise AI platforms, learning how to bridge traditional software architecture with modern AI capabilities without disrupting existing business operations.
Evolved from integrating existing AI tools to building custom AI systems for specific enterprise needs. Developed TARS Health Companion for NASA Space Apps Challenge, demonstrating AI could provide genuine emotional support rather than just data processing. Built production systems spanning healthcare FHIR integration, educational technology, and consumer applications.
This period focused on moving AI from experimental to production-ready systems that solve measurable business problems while maintaining regulatory compliance.
As AI systems become critical infrastructure, I've focused on the intersection of AI innovation and security/compliance. Building enterprise platforms for EU AI Act compliance, manufacturing operational protection, and regulatory automation. This combines technical AI expertise with business impact and risk management.
Current focus includes scaling systems with $1-9M revenue potential and preparing for the 2026 regulatory landscape while maintaining operational excellence for enterprise clients.
Every AI system I build starts with a human need. Not "how can we use this new model?" but "what genuine problem are people facing?" This leads to more useful, ethical, and sustainable systems.
Demos are interesting, but production systems that people actually use teach you what really matters. HIPAA compliance, real-time performance, failure handling, user support - these constraints make better systems.
AI doesn't exist in isolation. Every system I build connects to existing workflows, regulatory requirements, human psychology, and business constraints. The best AI solutions work within these systems, not against them.
Ethics isn't an afterthought - it's built into system architecture from day one. Privacy by design, algorithmic transparency, human oversight, consent mechanisms, and bias monitoring are core technical requirements, not nice-to-haves.
Based in Austin, Texas, I work with local organizations to implement AI systems that enhance human capabilities rather than replace jobs. Through Austin Community College partnerships and MSP client work, I focus on practical AI adoption that creates value for local businesses and workers.
Current enterprise focus includes scaling compliance automation platforms for the 2026 regulatory environment, healthcare FHIR integration for major providers like HCA, and manufacturing operational protection systems. These deployments demonstrate market-ready AI solutions with measured business impact.
Working to bridge the gap between cutting-edge AI research and practical business implementation. This includes developing educational resources that help traditional developers understand AI integration and helping organizations adopt AI responsibly.
I'm currently scaling the AI compliance automation platform to serve enterprise clients navigating the 2026 EU AI Act requirements. This involves building systems that can automatically assess AI system risk levels, generate compliance documentation, and provide ongoing monitoring for regulatory changes.
I'm also expanding TARS Health Companion from space applications to other isolated environments - Antarctic research stations, submarine deployments, and remote work situations where psychological support is critical.
I believe in shipping working systems early and often. Every project includes comprehensive testing, security auditing, and performance monitoring from day one. I prefer simple solutions that work reliably over complex ones that impress technically.
Documentation and knowledge sharing are core parts of my process - not just for others, but to ensure I understand what I'm building and why. Every system includes architectural decision records, deployment guides, and lessons learned.
I see AI engineering as fundamentally about understanding human needs and building systems that serve them responsibly. This requires not just technical skills, but empathy, ethics, business understanding, and domain expertise in areas like healthcare, education, and cybersecurity.
The most important question isn't "what can this AI do?" but "what should it do, and how do we ensure it does it safely and ethically?"