Resumeet: An Intelligent Resume Analysis and Recruitment Matching System Based on Large Language Models

An open-source intelligent recruitment tool that uses the Google Gemini 2.5 Flash large language model to achieve precise resume parsing and job matching scoring, providing in-depth semantic analysis and improvement suggestions for job seekers and recruiters.

In today's highly competitive job market, resume screening has become the most time-consuming and bias-prone环节 in the recruitment process. Traditional resume parsing tools often rely on keyword matching and cannot understand the depth and context of a candidate's experience. Resumeet AI emerged as a sophisticated intelligent recruitment tool that bridges the gap between candidate profiles and job requirements. Through large language models (LLMs), it deconstructs complex resumes into actionable data points, providing recruiters and job seekers with clear "matching scores" and strategic improvement roadmaps.

Resumeet adopts a modern technical architecture to ensure efficient operation and scalability of the system:

• Intelligent Core: Google Gemini 2.5 Flash (Large Language Model)
• Backend Framework: Python / Flask
• AI SDK: Google GenAI (Modern client-first architecture)
• Document Parsing Engine: PyPDF2
• Frontend Interface: HTML5 / CSS3 (Supports responsive dark mode)

This combination of technologies reflects the best practices in current AI application development—lightweight backend paired with powerful cloud-based LLM capabilities, ensuring both system response speed and full utilization of cutting-edge natural language processing technology.

Unlike traditional parsers that rely on keyword stuffing, Resumeet can understand the intent of experience and the depth of qualifications relative to job descriptions. This means the system can not only identify the word "Python" in a resume but also understand the candidate's actual proficiency in Python, the complexity of their project experience, and the relevance of these experiences to the target position.

The system can instantly identify missing technical and soft skill keywords that are crucial for passing applicant tracking system (ATS) screening. This feature directly addresses one of the most common pain points in modern recruitment—many qualified candidates are filtered out by automated screening systems due to resume format issues or missing keywords.

Resumeet provides candidates with high-impact, actionable suggestions to help them optimize their profiles for specific positions. This feedback is not vague advice like "improve skills" but precise recommendations based on specific job descriptions, such as "add descriptions of Docker and Kubernetes experience" or "strengthen team collaboration cases.

The system uses optimized extraction logic to handle complex multi-column resume layouts. This is particularly important because modern resume designs are becoming increasingly diverse, and traditional text extraction tools often fail when dealing with creative layouts.

The core output is the "matching score"—a quantitative metric that直观ly shows the candidate's fit with the position. This score is not a simple keyword count but a comprehensive evaluation calculated based on the large language model's deep semantic understanding of the job description and resume content.