AI Development
I build AI-powered features and LLM-driven workflows — from automation and data pipelines to intelligent tooling that turns business requirements into production-ready systems.
Hello, I'm
Senior Full-Stack Developer specializing in AI development, full-stack engineering, and cloud engineering.
I build AI-powered features and LLM-driven workflows — from automation and data pipelines to intelligent tooling that turns business requirements into production-ready systems.
I deliver scalable, production-grade systems end to end — React and Next.js front-ends, Node, PHP, and .NET back-ends, and the REST APIs that connect them, built for performance and maintainability.
I architect and deploy on AWS and Google Cloud with CDN-backed, automated CI/CD pipelines — optimizing reliability, scalability, and delivery speed across environments.
Front-End Developer
Co-founder & Tech Lead
Full Stack Developer
Front End Developer
Professional Certificate
Diploma In Web Development
AWS Machine Learning Scholarship
Computer Science
AI can write the code. The harder problem is building systems you can trust to write it.
Over the last several months, I have been developing my agentic engineering practice by building and testing production systems where AI agents handle much of the implementation, while I focus on architecture, verification, guardrails, and the decisions around them.

Putting Sentinel to the Test in Production
A real production environment for testing how far agentic engineering can go beyond prototypes: video-first discovery, live quoting, payments, dispatch, and lifecycle email. This is not a demo. It processes real payments for rides operated by a licensed transportation provider in Jamaica, so the engineering has to account for real-world failure modes.
The system includes
~50%Roughly 50% of customers now arrive through ChatGPT

Design Intelligence as Infrastructure
Models can generate UI that works. Getting them to consistently apply good design thinking before they start building is a different challenge. Sentinel is a design agent exposed through MCP that a model can call before it builds anything visual.
It brings design thinking in through
Shared memory
Every design Sentinel analyzes and every score it produces feeds shared memory, so each iteration starts with more context than the last.
589258 to 92 score on one production page after rebuilding against its recommendations
The biggest lesson
Generation is only one part. Fundamentals and engineering best practices still matter.
AI can accelerate implementation, but it does not remove the need for architecture, testing, security, CI/CD, observability, documentation, design thinking, and disciplined engineering practices. If anything, those fundamentals become more important when agents are producing more of the code.