Job Portal with AI Resume Ranking

Project Domain / Category

Web Application

Abstract / Introduction

The Job Portal with AI Resume Ranking is an intelligent recruitment system designed to match job seekers with the most relevant job listings using Artificial Intelligence (AI) and Natural Language Processing (NLP). The system allows candidates to upload their resumes, which are then analyzed and ranked based on job descriptions using AI-powered resume parsing and matching algorithms. Recruiters can post jobs, filter candidates, and receive an automatically ranked list of the best- matching applicants. The system aims to improve hiring efficiency by reducing manual resume screening and ensuring better job-to-candidate matching.

Detailed Functional Requirements:

1.User Management:

·       Job Seeker Module:

o   Register/Login (Google, LinkedIn authentication)

o   Upload and edit resumes (PDF, DOC formats)

o   Apply for jobs and track application status

·         Recruiter Module:

o   Register/Login as an employer

o   Post job listings with detailed descriptions

o   View AI-ranked candidate applications

·         Admin Module:

o   Manage job postings, users, and system settings

o   Monitor AI performance and refine algorithms

2.AI-Based Resume Ranking:

AI-based resume ranking is the core feature of an intelligent job portal that enhances the hiring process by automating resume screening and matching candidates to job descriptions efficiently. Below is a detailed breakdown of how it works:

a.Resume Parsing

Before ranking resumes, the system needs to extract and structure the information from various resume formats (PDF, DOC, etc.). This process is called Resume Parsing and involves:

Steps in Resume Parsing:

1.    Text Extraction:

o   Extract text from different resume formats using tools like PyMuPDF, Apache Tika, or PDFMiner.

2.    Data Structuring:

o   Identify and extract structured data from the resume, such as:

§  Personal Information (Name, Email, Phone, Location)

§  Work Experience (Job Titles, Companies, Years of Experience)

§  Education (Degrees, Universities, Graduation Years)

§  Skills (Programming languages, tools, soft skills)

§                       Certifications & Projects

3.    Named Entity Recognition (NER):

o   Use NLP techniques (Spacy, NLTK, or BERT) to identify key entities (e.g., "Python Developer" as a job title, "Harvard University" as an institution).

4.    Keyword Extraction:

o   Extract important terms using TF-IDF (Term Frequency-Inverse Document Frequency) to understand relevant keywords in the resume.

b.Job Matching Algorithm

Once the resume is parsed, the AI compares the extracted information with the job description to find the best matches. Use the BERT Model for Job Matching.

BERT (Bidirectional Encoder Representations from Transformers)

·       A powerful Deep Learning model for contextual understanding.

·       Unlike TF-IDF and Word2Vec, BERT understands full sentence meaning.

·       Example: If a job description says "Seeking a software engineer with expertise in cloud technologies",

o   A resume with "AWS, Azure, Cloud Computing" will match strongly, even if

exact words don’t match.

c.  Skill-Based Filtering

Even if resumes are ranked based on overall relevance, recruiters might want to filter candidates based on specific skill sets.

How Skill-Based Filtering Works:

1.    Technical Skill Matching:

o   The system extracts and ranks hard skills (e.g., Python, React.js, Docker) based on job requirements.

o   Example: If a Data Science job requires "Python, TensorFlow, SQL", resumes missing these will have a lower ranking.

2.    Soft Skill Matching:

o   Extracts behavioral skills (e.g., teamwork, leadership) from cover letters or resumes using Sentiment Analysis.

o   Example: A Project Manager role may prioritize "Leadership, Communication, Time Management".