Technology Leader · AI Systems & Product Engineering
I build production-grade AI systems, developer tooling, and scalable software products. My work bridges frontier AI infrastructure—agent runtimes, protocol design (MCP), and reliability benchmarking—with disciplined full-stack product engineering and technical leadership.
Tools and infrastructure focused on agent reliability, AI observability, and software supply-chain security:
| Project | Focus | Description |
|---|---|---|
| KaziAI-Bench | Agent Reliability | Production-grade benchmark and evaluation harness measuring AI agent reliability, deterministic task execution, and failure modes. |
| VibeCoder-Production-Checker | AI Code Governance | Automated scanner auditing AI-generated code for security vulnerabilities, configuration oversights, and production readiness. |
| Deplyze | Supply-Chain Intelligence | Dependency intelligence and vulnerability auditing platform built for modern codebases and AI coding agents. |
| ai-cost | AI FinOps & Observability | Open-source usage, token economics, latency, and model observability toolkit for production LLM deployments. |
| MCP-Server-Starter-Kit | Agent Tooling & Protocols | Battle-tested Model Context Protocol (MCP) server foundation to securely expose APIs, data, and tools to AI agents. |
| AI-Eval-Kit | Model Evaluation | Open-source regression testing, synthetic evaluation, and continuous quality assurance for LLM applications. |
| Area | Leadership Focus |
|---|---|
| AI Systems & Strategy | Architecting robust AI agent architectures, evaluation harnesses, and secure tool integration protocols (MCP) that translate research into business value. |
| Product Engineering | Translating user workflows, complex domain logic, and data into performant, intuitive products designed for longevity. |
| Experience & Design Systems | Directing UI/UX engineering, developer ergonomics, and design systems with high accessibility and usability standards. |
| Engineering Discipline | Instituting automated testing, supply-chain auditing, deterministic benchmarks, and reliable CI/CD delivery pipelines. |
| Executive Communication | Demystifying AI and technical complexity into clear business trade-offs, risk assessments, and strategic roadmaps. |
I stay close to the code to design resilient architectures, evaluate engineering trade-offs, and build with conviction.
- AI Systems & LLMOps: Autonomous Agents, Model Context Protocol (MCP), LLM Benchmarking & Evaluation (
KaziAI-Bench,AI-Eval-Kit), AI Cost & Token Observability (ai-cost), Prompt Registries, AI Code Quality Scanning - Product & Experience: TypeScript, Next.js, React, Tailwind CSS, Radix UI, Framer Motion, Design Systems, Accessibility (a11y)
- Engineering & Architecture: Node.js, Express, Laravel, PHP, REST APIs, MySQL, Data Modeling, Distributed Systems
- Platform & Security: Docker, Software Supply-Chain Auditing (
Deplyze), CI/CD Pipelines, Git Workflows, Firebase, Linux
- Make AI deterministic where it counts: Counteract stochasticity with rigorous benchmarks, regression evaluations, and sanity checks.
- Lead with outcomes: Every architectural and tooling choice must connect directly to measurable operational and business impact.
- Design for people first: Tools and products succeed when developer experience (DX) and user experience (UX) reduce cognitive friction.
- Shift security & reliability left: Supply-chain integrity, cost observability, and production readiness are core design requirements, not post-launch patches.
- GitHub: @MosetiReagan
- Email: mosetireagan78@gmail.com