Intelligent Resume Screener using NLP + RAG

1️⃣ Objective

The main objective of this project is to design and develop an AI-powered resume screening system that automatically evaluates, shortlists, and ranks candidates based on job descriptions using Natural Language Processing (NLP) and Retrieval-Augmented Generation (RAG) techniques.

Key Goals:

✨Automate the manual resume screening process.

✨Accurately extract and interpret candidate skills, education, and experience.

✨Match candidate resumes with job requirements using semantic similarity.

✨Generate insights or summaries explaining why a candidate fits (or doesn’t fit) a role.

✨Enhance the recruitment workflow with faster and more intelligent decision-making.

2️⃣ Problem Statement

Traditional resume screening is time-consuming and prone to human bias. HR teams often deal with hundreds of resumes per role and may overlook suitable candidates due to keyword mismatches or fatigue.

This project aims to solve this issue by implementing an AI-driven system that uses context-aware language models and retrieval-based reasoning to rank candidates based on relevance and competency.

3️⃣ Methodology

The project will follow the following step-by-step approach:

✨ Step 1: Data Collection: Collect a dataset of resumes and job descriptions. Preprocess and anonymize data.

✨ Step 2: Data Preprocessing: Convert resumes into structured text. Clean and tokenize, and extract key entities using NER (spaCy/NLTK).

✨ Step 3: Feature Engineering: Represent resumes and job descriptions as embeddings using transformer models (e.g., Sentence-BERT). Compute semantic similarity scores.

✨ Step 4: Retrieval-Augmented Generation (RAG): Build a vector database (FAISS/Chroma/Pinecone) to store embeddings. Use an LLM (GPT/Llama) to summarize and explain the candidate match with contextual reasoning.

✨ Step 5: Ranking and Scoring: Rank candidates using semantic similarity, experience/skill match, and educational weightage to generate an overall suitability score (0–100%).

✨ Step 6: Evaluation: Validate ranking results against HR-labeled data. Adjust scoring weights.

✨ Step 7: Interface / Deployment: Create a simple web dashboard (Streamlit/Flask) for viewing ranked candidates and AI-based reasoning reports.

4️⃣ Dataset

Sources:

✨ Public resume datasets (e.g., Kaggle).

✨ Synthetic data generated for training (to avoid privacy concerns).

✨ Scraped or manually collected job descriptions.

Data Fields:

Resume Attribute	Description
Name	Candidate’s full name
Skills	Technical and soft skills
Experience	Total years of experience
Education	Degree, university, and specialization
Projects	Key projects or accomplishments
Certifications	Relevant credentials
Job Description	Role requirements and expectations

5️⃣ Tools and Technologies

Category	Tools / Libraries
Language Processing	spaCy, NLTK, HuggingFace Transformers
Embedding & Similarity	Sentence-BERT, FAISS, OpenAI Embeddings
RAG Implementation	LangChain, Chroma / Pinecone
Backend / API	Flask / FastAPI
Frontend (Optional)	Streamlit / React
Data Handling	Pandas, NumPy
Model Evaluation	Scikit-learn, Matplotlib
Deployment (Optional)	Docker, Streamlit Cloud, or AWS

6️⃣ Evaluation Metrics

✨ Precision: Accuracy of shortlisted candidates.

✨ Recall: Coverage of suitable candidates found.

✨ F1-Score: Overall performance balance.

✨Cosine Similarity Score: Semantic alignment between resume and job description.

✨ HR Validation Accuracy: Human evaluation benchmark.

✨ Response Relevance Score (for RAG): How well the model explains candidate-job fit.

7️⃣ Deliverables

Deliverable	Description
Preprocessed Resume Dataset	Clean and structured resume data ready for model training
NLP Pipeline	Code for text extraction, cleaning, and entity recognition
Embedding & Retrieval System	Vector database with semantic search capability
RAG-based Resume Analyzer	Model capable of context-based resume evaluation
Candidate Ranking Dashboard	Interactive interface to visualize results
Final Report & Documentation	Summary of methodology, results, and evaluation metrics

8️⃣ System Architecture Diagram

Visual representation of the system architecture (data flow from resume upload to RAG-based reasoning):

Input: Resumes & Job Descriptions

Diverse formats (PDF, DOCX) & text-based job specs

Resume Parser & Preprocessor

Text extraction, cleaning, entity recognition (skills, experience)

Embedding & Indexing

Vector embeddings (resumes & job descriptions) into Vector DB

↓

Recruiter Query / Job Match Request

Natural language questions or JD for candidate matching

Retrieval-Augmented Generation (RAG)

Vector search & context retrieval from relevant resumes

LLM-Powered Response & Scoring

Generates answers, summaries, match scores, and rankings

↓

Output: Shortlist, Answers & Insights

Ranked candidates, detailed fit analysis, query answers, bias checks

1. Input: Resumes & Job Descriptions

Diverse formats (PDF, DOCX) & text-based job specs

↓

2. Resume Parser & Preprocessor

Text extraction, cleaning, entity recognition (skills, experience)

↓

3. Embedding & Indexing (Vector Database)

Vector embeddings (resumes & job descriptions) into Vector DB

QUERY STARTS HERE

4. Recruiter Query / Job Match Request

Natural language questions or JD for candidate matching

↓

5. Retrieval-Augmented Generation (RAG)

Vector search & context retrieval from relevant resumes

↓

6. LLM-Powered Response & Scoring

Generates answers, summaries, match scores, and rankings

↓

7. Output: Shortlist, Answers & Insights

Ranked candidates, detailed fit analysis, query answers, bias checks

9️⃣ Expected Outcome

✨ Automated resume screening with intelligent ranking.

✨ Reduced HR workload and improved decision accuracy.

✨ Transparent, explainable candidate evaluations through RAG summaries.

✨ Scalable system ready for integration into recruitment platforms.

Contact Info