Backend & Frontend Developer for Lab Management Application

Backend & Frontend Developer for Lab Management Application

Backend & Frontend Developer for Lab Management Application

Upwork

Upwork

Remoto

1 day ago

No application

About

We are looking for a skilled full-stack developer to join our ongoing project — a laboratory sample management, purchasing, and reagent tracking web application. The system is already built and operational, and we are continuously adding new modules and improving existing ones. This role involves iterative feature development, rapid prototyping, and close collaboration with the lead developer (myself). Responsibilities -Design and implement new modules for sample management, procurement, and reagent tracking. -Write clean, maintainable Python/Flask backend code and well-structured HTML/JS frontend components. -Work closely through GitHub — contributing via branches, pull requests, and code reviews. -Debug, test, and deploy updates efficiently. -Communicate progress clearly and iterate quickly based on feedback. Requirements -Strong proficiency in Python (Flask framework). -Good command of HTML, JavaScript (vanilla JS or lightweight frameworks), and CSS. -Familiarity with SQLAlchemy and relational databases (PostgreSQL or SQLite). -Experience with Git and GitHub workflows (branches, PRs, merges). -Comfortable working on existing codebases and adding new features incrementally. -Excellent debugging and problem-solving skills. -Strong communication skills and ability to work independently with guidance. Important note on AI-Assisted Development Expectation We encourage and expect the candidate to make efficient use of AI-assisted coding tools (e.g., GitHub Copilot, ChatGPT, or other LLM-based assistants) to accelerate development, code integration, and debugging. The focus is on: -Rapid prototyping and implementation of functional modules using AI-assisted suggestions. -Applying critical judgment to review, refine, and debug AI-generated code for reliability and maintainability. -Using AI tools to generate or refactor code snippets, improve documentation, and accelerate troubleshooting. -This role is not about relying blindly on AI output, but about combining technical expertise with AI-driven efficiency to deliver robust features quickly.