AI APPLICATION ENGINEER
Building full-stack AI applications
From architecture to AI, I build intelligent products with system thinking and full-stack engineering.
6
Years in Architecture
4
Core Portfolio Projects
800+
Hours AI Learning & Building
My background in architecture trained me to think in systems, structure, and user experience.
Now I apply that mindset to AI products: clarify the problem, design the flow, build the system, and ship the result.
Focus on designing reliable systems, integrating modern technologies, and delivering practical software solutions.
Turning ideas into AI-powered products from concept to deployment.
Building complete frontend, backend, and data systems.
Building intelligent systems with LLMs, structured output, and automation.
Turning activity data into insights and measurable decisions.
Building intelligent assistants that improve engineering workflows.
AI applications, full-stack products, and agent experiments demonstrating end-to-end capability from concept to delivery.
A single-user full-stack AI application that turns real study and development logs into reviewed Daily, Bug, Evidence, and project records through a Source → AI Extraction → Candidate Review → Accept → Publish workflow.
A read-only LangGraph Single Agent with v1.6 generic source indexing and deterministic evidence retrieval. The controlled demo completed 5 of 7 diagnoses; 2 safely entered Human Review when evidence was insufficient.
A four-module WeChat Mini Program MVP with DeepSeek writing feedback and a recording → cloud storage → Tencent ASR → text-based speaking feedback pipeline. Reading and listening use local scoring.
A public Next.js portfolio featuring a state-driven Explore Me journey, a section-based story flow, and responsive validation across desktop, iPhone, Android phone, and Android tablet.
A structured engineering workflow that transforms ideas into AI products through research, architecture, development, and iteration.
Understand users, problems, and real-world scenarios.
Create PRD, user flows, and acceptance criteria.
Define system architecture, data flow, APIs, and AI workflow.
Implement frontend, backend, data systems, and AI features.
Deploy products, fix issues, and continuously iterate.
A curated ecosystem of technologies I use to build intelligent products.
The core technologies and tools I use to design, build, and ship intelligent applications.
Building responsive interfaces and reusable component systems.
Designing APIs, authentication, and maintainable application architecture.
Building structured LLM workflows and developer-controlled AI applications.
Managing structured data, schema evolution, and application storage.
Deploying reliable products from development to production.
Using AI-assisted development, testing, debugging, and release validation.
I combine product thinking, engineering execution, and AI capabilities to build practical software products.
Starting from user needs and real outcomes, not from writing code.
Delivering end-to-end products across frontend, API, database, and deployment.
Shipping practical AI features — prompt systems, structured output, and workflow automation.
A structured process from PRD and architecture to implementation and iteration.
Keeping goals, scope, and trade-offs clear for predictable collaboration.
Driving real product outcomes, not just shipping code, with continuous improvement.
From Architecture Background to AI Application Engineering, I'm open to opportunities, collaborations, and building intelligent products.
Have an idea, an opportunity, or a product challenge? I'd love to hear what you're building.
Architecture Thinking→AI Engineering→Product Building
