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  • San Jose, CA
  • 14:04 (UTC -07:00)
  • LinkedIn in/xzyuan

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X1nzhe/README.md

Hi there 👋, I'm Xinzhe Yuan

Software Engineer focused on backend systems, distributed APIs, agentic tools, and LLM-powered applications.

I build production-oriented AI software systems with Python, Flask, FastAPI, AWS, semantic search, browser automation, and LLM orchestration.

🧠 What I Do

  • Build backend services for LLM-powered applications and AI products
  • Design RESTful APIs, async task pipelines, semantic search, and RAG systems
  • Optimize LLM workloads for cost, latency, token usage, and retrieval quality
  • Develop agentic browser automation tools using Chrome DevTools Protocol
  • Ship and monitor cloud-based services on AWS

🚀 Featured Projects

🔹 Currents — Intelligent Social Search Engine · currents.one

A search engine that surfaces how online communities discuss products, brands, and topics, turning scattered social content into structured insight.

  • Built and extended backend services for LLM-powered semantic search using FastAPI and AWS
  • Designed asynchronous task-processing pipelines with SQS, Lambda, S3, and Boto3
  • Refactored keyword-extraction logic, reducing third-party LLM API calls by 66%
  • Migrated vector storage from Milvus to AWS S3 Vector DB, reducing infrastructure cost by $400/month while maintaining retrieval quality
  • Added CI/CD and production observability with GitHub Actions and CloudWatch

Tech: Python · FastAPI · RAG · Embeddings · MySQL · AWS EC2 · SQS · Lambda · Boto3 · S3 Vector · DynamoDB · CloudWatch


🔹 Extends — Autonomous Web Agent · extends.one

An LLM-powered Chrome extension that helps users automate multi-step web workflows through natural language.

  • Built browser automation features using Chrome DevTools Protocol, Chrome Extension APIs, and LLM-driven task execution
  • Developed orchestration components for planning, tool execution, recursive summarization, semantic filtering, and PDF parsing
  • Improved context handling to reduce token usage and improve response relevance
  • Integrated AWS AgentCore Memory for cross-session personalization
  • Evaluated the agent on WebVoyager-style browser automation tasks across dynamic websites

Tech: TypeScript · React · Next.js · Flask · Chrome DevTools Protocol · OpenAI SDK · Gemini SDK · AWS

🔗 Eval repo: Virtu-llc/web_agent_eval


🛠️ Stack

Backend & Infrastructure: Python · FastAPI · Flask · REST APIs · Redis · MySQL · PostgreSQL · Docker · Linux
AI & LLM Systems: OpenAI SDK · Gemini SDK · RAG · Embeddings · Agent Orchestration · Context Management · Prompt Engineering
Cloud & DevOps: AWS EC2 · S3 · SQS · Lambda · DynamoDB · CloudWatch · GitHub Actions
Vector & Retrieval: Milvus · AWS S3 Vector DB · AWS AgentCore Memory · Semantic Search
Frontend & Web Tools: TypeScript · React · Next.js · Tailwind · Chrome Extension APIs · Chrome DevTools Protocol

📫 Connect

LinkedIn · xzhe121@gmail.com

Pinned Loading

  1. NEU-INFO6250-Web-Dev NEU-INFO6250-Web-Dev Public

    This course covers fundamental and modern web development practices, using JavaScript for both frontend and backend development.

    JavaScript 1

  2. claude-fastapi-guide-plugin claude-fastapi-guide-plugin Public

    Claude Code Plugin for guided FastAPI tour

  3. store_locator_service store_locator_service Public

    A FastAPI-based store locator API. It supports public store search by address, ZIP code, or coordinates, and internal store management with JWT authentication and role-based access control.

    Python