Alex Sun

Summary

Full-stack developer, AI engineer, DevOps specialist with over 5 years of experience, delivered more than 20 projects for clients such as Tesla, Nissan, YanFeng, CASC, CASIC. Proficient in building responsive cross-platform applications, frontend to backend, with end-to-end DevOps support. Possesses strong team collaboration and client communication skills, innovative and critical thinking.

Education

TECHNICAL SKILLS

Frontend:    TypeScript, React, Next.js, Vue, Tailwind, PWA, Wordpress, Capacitor.js
Backend:     Prisma, FastAPI, NestJS, SpringCloud, PostgreSQL, MongoDB, Redis, RabbitMQ
Cloud:       AWS, GCP, Cloudflare, Supabase, Upstash, Sanity, Vercel
DevOps:      Github Actions, Docker, Kubernetes, Jenkins, GitLab, Postman
3D:          Three.js, Spline, WebGL/webGPU, Unreal Engine (MetaHuman related)
AI Workflow: Claude Code, Lovable, PyTorch, OpenCV, TensorFlow, RetellAI
AI Skills:   Context Engineering, n8n, A2A, MCP, RAG, Fine-tuning

Experience

Full-stack Software Developer

Founded and developed Learnify and Anify. Take part in the development of Ringbot.

Senior Software Engineer

Led a technical team of 22 people at the Nanjing R&D Center, reporting directly to the CTO. Directed the restructuring of the team into project-based units, adopted agile methodologies, and introduced AI-assisted programming to all teams, resulting in a 60% increase in development efficiency. Implemented stand-up meetings, code reviews, automated DevOps workflows and containerized deployment, achieving an 85% reduction in customer-reported issues.

Software Engineer

I contributed to the development of a B/S architecture PLM system, where I played a key role in the development of many core functions.

Software Developer

Developed many Siemens Teamcenter projects with more than 100 complex functional modules. Developed several B/S projects.

Project

Learnify Alpha Version: https://learnify.oiy.ai

Description

An AI-powered learning tool that can help users learn everything faster and better.

User can upload files in different formats (PDF, Word, Excel, PPT, Images, Youtube Links, etc) and Learnify will generate syllabus, mind maps, summary notes, flashcards, quizzes, podcasts and more.

What's even better is user can get 1v1 teaching experience by rasing inline AI chats in Notes, asking for detailed explanation in Quizzes, joining podcast as a guest then starting a real-time chat with the host.

My Contribution

It's an open-source project (https://github.com/oiy-ai/learnify). I founded it by myself and contributed everything. Besides feature development, I also:

• designed the technical architecture
• conducted research on the technical selection of every modules
• built the github actions pipeline for all services
• deployed the services to GCP, Cloudflare and a bunch of other cloud native services.

Tech Stack

Frontend: React, Next.js, Tailwind, PWA, Lit, Electron, Capacitor

Backend: Prisma, NestJS, PostgreSQL, pgvector, GraphQL, Redis, webRTC

AI: OpenRouter, Claude Code, IndexTTS, LangGraph, pgvector, Vertex AI

DevOps: Docker, Kubernetes, Github Actions, GCP, Cloudflare, Supabase, Upstash

Anify (WIP)

Description

It's an AI companion with:

• Excellent modeling and rendering based on UE5 MetaHuman.
• Free conversation interspersed with interactive storylines.
• With a high-freedom parameter system, AI will determine the favor, identity, relationship, shared experiences between itself and the user.
• Real-time expressions, actions, and clothing changes based on agentic system.

My Contribution

I assembled the entire Oiy AI team, with 2 Monash students and 2 engineers from Amazon (Sydney) and Mecca (Melbourne).

Me myself was in charge of DevOps, AI workflow design, low latency audio-to-audio AI.

Tech Stack

Client: Unreal Engine, MetaHuman Creator, MetaHuman Animator

Backend: TypeScript, C++, PostgreSQL, Redis, RabbitMQ, WebRTC

AI: Stable Diffusion, ComfyUI, IndexTTS, LangGraph, HuggingFace, Gemini Live API

DevOps: Docker, Github Actions, Cloudflare, Supabase, Upstash, Stripe

Ringbot (WIP) Landing Page: https://ringbot.au

Description

Ringbot is an AI VoiceAssistant for Effortless Reservations. She has a natural voice and can help users to make reservations on phone calls or web voice chat.
It's built by a startup company in Melbourne. And they are building the prototype with several local enterprises like Okami, MC Dental.

My Contribution

I was in charge of the frontend development, including the voice chat interface, dashboard for clients and the landing page with a voice chatbot.

Tech Stack

TypeScript, React, Next.js, Tailwind, PWA, Radix UI, shadcn/ui, Magic UI, webRTC

Wonder Insight Demo: http://plm-test.plm.wonder10.com (authorization required)

Description

A self-developed Product Lifecycle Management (PLM) platform. Benchmarking against Siemens Teamcenter, it covered over 70% of the features and in most features it's faster than Teamcenter. It earned over 10 million RMB in enterprise contracts within the first year after launch.

My Contribution

I'm the technical lead of this team. I'm responsible for the technical architecture, system design, and development of the core features. I also in charge of the agile iteration, weekly public release, and delivery management.

Tech Stack

Frontend: TypeScript, Vue, Electron, WebGL, Three.js, Micro-frontend
Backend: SpringCloud, MySQL, MongoDB, Redis, RabbitMQ, Kafka
DevOps: Docker, Kubernetes, Gitlab, Jenkins

Light Viewer Demo: https://viewer.xsun.work

Description

A cross-platform viewer for 3D models. It's focused on manufacturing industry, with a powerful JT format support. It's main features are:

• Carry parameters and annotations with the model
• Support multi-file model rendering based on BOM. Based on this, when customers update the huge BOM structure, they do not need to update the entire model file. In most cases, this will save more than 90% of the time
• Dynamic rendering and hot-reload. When user changed one part of a huge model, the viewer will only render the changed part and update the entire model in 1s.
• Virtualized rendering. Support giant model with over 100,000 parts and 100,000 faces without lagging.

My Contribution

I'm the developer of this project. I built each feature from scratch and responsible for the entire development process.

Tech Stack

Three.js, WebGL, Vue, Siemens JT Toolkit, Docker

Wonder Insight Industry Projects

Delivered many enterprise-level PLM/PDM projects based on the Wonder Insight platform. Client list includes leading companies in the aerospace, aviation, and machinery industries. Some of the projects are listed below:

• Lunar Exploration Program(CLEP) Project Management System project
• CASIC Data Package Management (DPM) project
• CASC Intelligent process preparation project

My Contribution

As the leader of the product development team, provide project technical support and communicate with the implementation team.

Tech Stack
Same as Wonder Insight.

Teamcenter Industry Projects

Delivered many enterprise-level PLM/PDM projects based on Siemens Teamcenter. Client list includes leading companies in the aviation and machinery industries. Some of the projects are listed below:

• Nissan Wuhan Factory Teamcenter Manufacturing (TCM) project
• Shaanxi Xi'an Fast Automobile Teamcenter Manufacturing (TCM) project
• Shanghai Tesla Factory Teamcenter Manufacturing (TCM) project
• CASIC Teamcenter Manufacturing (TCM) project
• Emerson Electric Product Data Management (PDM) Phase II project using Teamcenter
• CASC Teamcenter Manufacturing (TCM) project

My Contribution

Developed many features of those projects. In some projects, I'm in charge of one or two entire modules on my own.

Tech Stack
Teamcenter, Java, SWT, SpringBoot

More Projects

AI Manufacturing Process Generation: a system using LLM and RAG to generate manufacturing processes, involving data cleaning and annotation of over 10 years of historical process data.

Yanfeng LEMS project: I was in charge of two modules: abnormal monitoring of ambient light using OpenCV, failure analysis using RAG.

Wonder Microweb: a micro frontends framework that enables seamless integration of diverse frontend technologies such as React, Angular, Vue and Next.js base on Web Components. It offers advanced features including data communication, global context management, unified virtual routing, unified API, prefetching.

Wonder Table: a Vue table component using virtualization that supports the smooth display of 100,000+ rows and 100,000+ columns without pagination, with powerful functions like filtering, sorting, editing, dragging, tree data support etc.