AI Resume Screening SaaS
An AI-powered recruitment platform that automates resume screening, candidate matching, and recruitment workflows using FastAPI, PostgreSQL, NLP, and intelligent filtering.
Overview
Developed a SaaS-based AI Resume Screening platform that streamlines the hiring process by automatically parsing resumes, extracting candidate skills, matching applicants against job requirements, and providing recruiters with searchable candidate profiles through a scalable FastAPI backend.
Business Problem
Recruiters spend significant time manually reviewing resumes, filtering unsuitable candidates, and comparing applicants against job descriptions. This process becomes increasingly inefficient as application volumes grow.
Solution
Designed a scalable recruitment platform where recruiters upload resumes, automatically extract candidate information using NLP, compare applicants against job requirements, and manage hiring workflows through secure REST APIs with PostgreSQL-backed storage.
Architecture
Key Features
- →AI-powered resume parsing and structured candidate extraction
- →Automatic skill extraction using NLP (spaCy)
- →Resume-to-job matching based on required skills
- →Secure recruiter authentication with JWT
- →Resume upload and candidate management
- →Advanced search and filtering of applicants
- →REST API architecture for frontend integration
- →Scalable PostgreSQL data model for recruitment workflows
Gallery
Showing complete working of the AI Resume Screening SaaS platform, including resume upload, skill extraction, and candidate search.
Tech Stack
Engineering Challenges
Reliable Resume Parsing
Resumes come in different layouts and formats, making information extraction inconsistent. Designed preprocessing logic and NLP pipelines to improve extraction accuracy across varying resume structures.
Skill Matching
Candidate skills often appear with different wording and abbreviations. Used NLP-based normalization to improve matching between resumes and job descriptions.
Scalable Backend Architecture
Designed REST APIs and database relationships that support future expansion into multi-company SaaS usage while maintaining clean separation between recruiters, jobs, and candidates.
Lessons Learned
- →Clean API architecture simplifies frontend integration and long-term maintenance.
- →NLP significantly improves recruitment workflows compared to simple keyword matching.
- →Proper database relationships are critical for scalable recruitment platforms.
- →Authentication and role-based access should be planned early in SaaS applications.
Results
Successfully built a functional AI-powered recruitment platform capable of automating resume screening, extracting candidate skills, and simplifying recruiter workflows through intelligent search and candidate matching.
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