Software Engineer (Full-Stack & AI-Assisted Development)

Responsibilities

1. Core Internal Application Development

• Independently design, build, and customize the company's OA system (workflows/approvals, attendance, announcements, document management).

• Participate in or lead the development of lightweight ERP modules (procurement, inventory, order management, and basic financial accounting).

• Rapidly iterate internal management tools based on business needs (e.g., equipment management, project management, asset management).

2. LLM-Driven Development

• Leverage AI coding tools such as Copilot, Cursor, and Claude Code to improve development efficiency.

• Apply prompt engineering to generate code, refactor, write unit tests, and interpret legacy code.

• Explore LLM applications in business scenarios (e.g., intelligent form filling, automated ticket classification, knowledge base Q&A).

3. Architecture & Implementation

• Independently handle technology selection, database design, API design, and full-stack development.

• Prefer practical, mature, maintainable, and well-documented technology stacks.

• Ensure robust implementation of permission models, logging, and baseline security.

4. Collaboration with IT Engineers

• Work with IT engineers (network/server) to complete deployment, backups, domain configuration, SSL setup, and other infrastructure tasks.

• Deliver documentation (deployment guides, user manuals, FAQs).

Requirements

1. Experience

• 3+ years of development experience.

• At least one independently delivered internal management system (e.g., OA, ERP, CRM).

2. Technical Stack (full-stack capability required; any of the following combinations is acceptable)

• Frontend: Vue / React, etc.

• Backend: Python (Django/FastAPI), Go, or Node.js

• Database: MySQL / PostgreSQL, with solid SQL skills

• Basic DevOps: Familiar with Docker; able to read and work with basic Linux documentation

3. LLM Capability

• Hands-on experience using AI coding assistants in daily development, with examples of productivity improvements (e.g., generating CRUD operations, API documentation, debugging).

• Not required to train models, but must understand LLM limitations and avoid over-reliance.

4. Business Understanding

• Ability to understand non-technical business requirements (finance, administration, production, etc.) and translate them into system designs.

5. Code Quality & Documentation

• Strong documentation habits and high code readability.

6. Communication & Ownership

• Proactively clarify ambiguous requirements instead of making assumptions or over-engineering.