ResumeMatch AI

Professional Resume Screening Platform Built for Recruitment Teams

ResumeMatch AI was developed as a resume-screening and candidate-ranking platform that automates hiring workflows. Designed to support growing recruitment operations, the platform helps recruiters review applications faster, reduce manual work, and shortlist candidates more efficiently.

Technology

Python, Flask, React.js

Industry

Business Services

Project Type

AI Recruitment Platform

Country

Poland

About The Project

ResumeMatch AI is a recruitment platform built for agencies and HR teams that manage large numbers of job applications. Many recruiters spend hours manually reviewing resumes, especially when hundreds of candidates apply for a single role. It slows hiring and makes it difficult to quickly shortlist the right candidates.

The platform was created to streamline and simplify the recruitment lifecycle. Recruiters can upload resumes in bulk, create job descriptions, automatically rank candidates, and generate interview shortlists from a single dashboard. The system also extracts key details, including skills, work experience, education, certifications, and industry keywords, from resumes.

Using machine learning and NLP technologies, the platform automatically compares resumes with job descriptions and generates candidate match scores.

Key Project Deliverables

Solutions were developed to improve candidate screening, streamline hiring workflows, and simplify recruitment management for HR teams.

Resume Parsing Engine

A resume parsing system was developed to extract candidate’s information from PDF and DOCX files using NLP-based text analysis.

Candidate Ranking Engine

A ranking system was created to compare resumes against job descriptions and automatically generate candidate match scores.

Backend Development

Flask REST APIs were developed for authentication, resume processing, candidate management, and analytics services.

Frontend Dashboard

A recruiter dashboard was designed to allow users to upload resumes, review rankings, manage candidates, and view reports.

Database & Infrastructure

PostgreSQL databases, Docker deployment environments, AWS hosting, and file storage systems were configured for scalable operations.

Secure Authentication

JWT authentication and role-based access controls were implemented to keep recruiter and candidate data secure.

Major Project Challenges

One of the main challenges was handling resumes uploaded in different formats and layouts. Candidates used different resume styles, resulting in inconsistent data extraction. The system needed to identify skills, education, certifications, and work experience accurately across all files.

Another challenge was improving skill-matching accuracy. Different candidates often use different words for the same skills. This affected the quality of recommendations and candidate rankings. The platform needed a better way to compare resumes with job descriptions beyond basic keyword matching.

Bulk resume uploads also created performance issues. Large uploads slowed down processing times and affected API performance. The platform needed to process resumes quickly while keeping the dashboard responsive for recruiters.

The ranking system also needed improvement to avoid poor recommendations. Some resumes contained many keywords but lacked relevant experience. The platform needed to balance keyword matching with actual work history so recruiters could trust the ranking results.

Solutions & Impact

ResumeMatch AI was built using Python, Flask, React.js, and NLP technologies with a clean and organized structure. It made the platform faster, easier to manage, and ready to support large recruitment workloads.

To improve resume parsing accuracy, NLP-based extraction pipelines were implemented along with semantic parsing and pattern recognition techniques. This helped the system extract skills, certifications, education, and work experience more accurately from different resume formats.

Built candidate ranking and job matching features.
Added semantic similarity analysis for better skill matching.
Implemented background processing for bulk resume uploads.
Optimized database queries to improve API performance.
Added weighted scoring for more balanced rankings.
Secured the platform using JWT authentication and access controls.
Developed recruiter dashboards for candidate management.
Deployed the platform using Docker and AWS infrastructure.

ResumeMatch AI now processes more than 50,000 resumes every month and has achieved 92% parsing accuracy. Resume screening time was reduced by 75%, helping recruiters review candidates much faster.