End-to-End AI-Driven Recruitment Platform: Predict Salaries with Machine Learning and Match Candidates to Jobs Using Large Language Models

The AI-powered-HR-Platform introduced in this article is an open-source end-to-end AI-driven recruitment platform maintained by eyamkaouar and hosted on GitHub (link: https://github.com/eyamkaouar/AI-powered-HR-Platform). Its core features include machine learning salary prediction, large language model-based candidate-job matching, and career growth analysis. It aims to solve problems like information asymmetry and low efficiency in recruitment using AI technology, enabling a full-link intelligent recruitment process.

In the highly competitive job market, enterprises and job seekers face the challenge of information asymmetry: enterprises struggle to quickly screen suitable talents, while job seekers are unclear about the market value of their skills. Traditional recruitment relies on manual screening, which is inefficient and highly subjective. The AI-powered-HR-Platform project emerged to reshape the recruitment process using AI technology, enabling full-link intelligence from resume parsing to salary prediction.

The project adopts a front-end and back-end separation architecture, consisting of four core modules: the back-end handles business logic and data persistence, providing RESTful APIs; the front-end is a user interaction interface supporting bidirectional operations for HR and candidates; the LLM module processes natural language understanding and generation tasks; the model module focuses on machine learning model training and inference (e.g., salary prediction). The project uses Docker containerization for deployment and can be started with a single docker-compose command, lowering the deployment barrier.

The platform has a built-in machine learning model that can predict a reasonable salary range for candidates in target positions based on multi-dimensional features such as their skill set, work experience, and educational background. This feature helps HR develop competitive compensation plans and allows job seekers to clearly understand their market value.

Leveraging the semantic understanding capabilities of large language models, the platform deeply parses job descriptions and resumes, identifying implicit skill correlations that traditional keyword matching cannot capture (e.g., the correlation between "familiar with distributed systems" and "microservices architecture experience"), thereby improving matching accuracy. Additionally, by analyzing data on successful career trajectories, the platform provides candidates with personalized career development advice, including skill improvement directions, potential job transfer opportunities, and target achievement timelines.

For enterprise HR: Shortens resume screening time from hours to minutes, and reduces subjective bias in recruitment decisions through data-driven salary recommendations. For job seekers: Provides career analysis to help make more informed career planning decisions.

The project uses a modular design for LLM integration, making it easy to switch between different language model backends; the machine learning part is optimized through feature engineering to ensure high accuracy of the salary prediction model across different industries and regions; the project is fully open-source, allowing community developers to customize and extend it based on their needs.

AI-powered-HR-Platform demonstrates the great potential of AI in the human resources field, enabling end-to-end intelligent recruitment processes by combining machine learning and large language models. With the improvement of model capabilities and data accumulation, such platforms are expected to become standard configurations for enterprise recruitment in the future, driving the human resources industry towards data-driven transformation.