Senior software engineer · 10+ years · Vue & React
Building AI-powered products. Recently shipped a production RAG chatbot that lets customers get answers in natural language instead of searching and skimming the help center.

Open to senior frontend or full-stack roles where AI is part of the product.

LinkedIn · Email · ryanchoi.dev

A production retrieval-augmented chatbot that ingests company documentation (including a live Zendesk knowledge-base sync via n8n) and answers end-user questions with streaming generation. Entire stack runs on the customer's infrastructure: embeddings and LLM inference via Ollama (Mistral + nomic-embed-text), pgvector for retrieval. No data leaves the box.

• Impact: Faster, easier documentation lookup for customers; conversational Q&A with streaming responses replaces searching and skimming through the help center.
• Stack: Nuxt 4 / Vue 3 · Nuxt server routes (Node) · PostgreSQL + pgvector · Drizzle · Ollama (Mistral + nomic-embed-text) · Redis · n8n for Zendesk sync · Docker Compose
• Notable engineering: hybrid retrieval with query rewriting, reranking, and RRF · prompt-injection defense pipeline (direct- and context-injection risk assessment, hard-block / soft-allow / sanitize modes, hardened system prompt) · streaming chat, message regeneration, feedback capture
• My role: Sole developer and designer on the project.
• Link: Private; happy to walk through in an interview.

Core Languages & Frontend

• TypeScript: Deepest expertise in Vue 3 and Nuxt; experienced with React and Next.js.
• Frontend Competencies: Component Architecture, Vue Composition API, React Hooks, State Management (Pinia, Redux), Client-Side Routing (Vue Router, React Router), and Server-Side Rendering.

Backend & Database

• Backend: Node.js (implemented via Nuxt server routes), PHP, Python, and C#.
• Databases & ORM: PostgreSQL (including pgvector for AI), MySQL, and MariaDB, interfaced via Drizzle ORM.

AI & Machine Learning Stack

• RAG Architecture: Shipped end-to-end pipelines featuring hybrid retrieval and prompt-injection defense.
• Self-Hosted Inference: Experience with Ollama and Mistral.
• Mobile AI: Currently building with on-device Gemma 4 E2B on iOS.

Tooling & Testing

• Build Tools: Vite
• Testing: Vitest (utilizing .spec.ts format for unit testing)

Team Philosophy

• Adaptable and happy to pick up whatever technologies the team uses.

iOS macronutrient tracker where users photograph a meal and get nutritional entries back from Gemma 4 E2B running on-device (via LiteRT-LM). No backend, no account, no network. Data stays on the phone. Built in Swift 6 with strict concurrency, SwiftData, and a dedicated ModelActor keeping inference off the main thread. Attacking the manual-entry friction that kills most food-logging habits.

_{Fastest way to reach me: ryan@ryanchoi.dev. Based in Minnesota, open to remote or hybrid.}