Full-stack engineer who orchestrates AI agents to ship faster without sabotaging code quality — with a background in HL7 healthcare interoperability that most AI-focused engineers don't have.
Not a laundry list of technologies — a specific stack of skills that reinforce each other.
Coordinating multi-agent systems (Hermes, OpenClaw) to build across a full stack without letting output degrade into unreviewed "vibe coding" — plus hands-on work running local LLMs (AnythingLLM, Ollama, LM Studio) to understand serving, parameters, and hardware tradeoffs first-hand.
Years of production HL7 interface engineering — building and maintaining clinical data pipelines with Mirth Connect, and more recently moving to native JS/REST integrations to move faster without breaking compliance.
PHP/Laravel, Node.js, React/React Native, Flutter/Dart — monorepo architecture, RBAC, MFA and Supabase Auth, and the infra underneath it, so an AI feature actually survives contact with real users.
Deep dives, not a project list. Details below are placeholders — drop in your real case studies and these slots update directly.
Problem: a private healthcare product needed an on-prem LLM server, a mobile app, an API, and an admin portal built together — without AI-assisted development collapsing into unreviewed "vibe coding."
Approach: orchestrated Hermes and OpenClaw agents across a single monorepo, enforcing OOP structure and closely auditing every agent output. Layered in MFA and Supabase Auth so speed never came at the cost of security.
Outcome: faster shipping cycles with fewer bugs reaching production than a fully manual build would have taken.
Problem: laboratory information system (LIS) integrations were built and maintained on Mirth Connect — reliable, but slow to extend as integration demands grew.
Approach: moved a significant share of new integration work from Mirth to native JS + REST API interfaces, deliberately structured to take advantage of AI-assisted coding speed without sacrificing HL7 compliance.
Outcome: [swap in — turnaround time per integration, number of interfaces migrated, etc.]
Problem: using AI APIs alone doesn't teach you why a model behaves the way it does, or what it actually costs to run one.
Approach: stood up local LLM servers across AnythingLLM, Ollama (CLI), and LM Studio for different applications — studying how parameter count, quantization, and hardware constraints trade off against each other in practice.
Outcome: firsthand understanding of LLM serving that now informs how agent workflows and validation are designed, rather than treating models as an opaque API call.
Mobile app, Play Store — community organization platform
Open-source admin panel boilerplate — GitHub
Mobile app, Play Store
A mobile companion for drivers — trip logging, vehicle health, and offline-first sync.
This is the part most portfolios skip. Here's what "AI validation" actually looks like in practice.
Grouped by what each layer is actually for — not just a badge wall.