📘 Introduction

Navigating job portals manually is tedious, inconsistent, and often inefficient. What if a system could read your resume, analyze your strengths, search jobs in real-time across platforms, and generate interview questions—all personalized to your career path? That’s exactly what this Agentic AI Job Assistant does.

Powered by LangChain, LLMs (GPT-3.5), RAG architecture, and orchestrated using CrewAI, this project is a full-stack intelligent assistant built with Streamlit that automates the end-to-end job discovery and interview preparation process.

---

🔍 Project Summary:

The Agentic AI Job Search Assistant is an intelligent, modular job-hunting platform that empowers candidates with real-time job recommendations, resume-based job matching, resume quality assessment, and AI-generated interview questions. Built using LangChain, CrewAI, OpenAI GPT, and web scraping APIs, it simulates a recruitment consultant with multi-agent large language model (LLM) workflows.

🎯 Project Goals

• Parse and analyze resumes using AI.

• Search real-time jobs from multiple platforms via SerpAPI.

• Match skills to jobs using semantic search.

• Generate targeted interview questions based on the job role and resume.

• Build an intuitive dashboard in Streamlit.

---

🧰 Tech Stack

---

🧠 Architectural Overview

                           +------------------------+
                           |     Streamlit UI       |
                           | (Upload, Filter, View) |
                           +------------------------+
                                      ↓
+------------------------+   +------------------------+   +------------------------+
| Resume Agent (LLM)     | → |    Skill Extraction    | → | FAISS Vector Embedding |
| PDF -> Skills/Sections |   |        Agent           |   | Compare JD ↔ Resume    |
+------------------------+   +------------------------+   +------------------------+
                                                   ↓
                                     +---------------------------+
                                     | Job Search Agent (SerpAPI)|
                                     | Fetch jobs by keywords &  |
                                     | location (LinkedIn/etc.)  |
                                     +---------------------------+
                                                   ↓
                          +-----------------------------------------+
                          | Job Match + Interview Q Generator (LLM) |
                          |         InterviewAgent (OpenAI)         |
                          |         LLM prompts for tailored        |
                          |         Interview Qs                    |
                          +-----------------------------------------+
                                                   ↓
                                   +-----------------------------+
                                   | Streamlit App: UI & Filters |
                                   +-----------------------------+

📂 Project Structure:

job_search_assistant/
├── app.py                # Streamlit front-end
├── config.py             # Env configs and styling
├── resume_agent.py       # Resume analysis logic
├── job_search_agent.py   # Job finder and matcher
├── interview_agent.py    # AI interview prep
├── ui_utils.py           # Streamlit display components
├── requirements.txt

---

🛠 Step-by-Step Implementation Guide

1️⃣ Resume Upload & Analysis

• Supports PDF, DOCX, TXT formats.

• Extracts raw text → Uses spaCy NER + Regex to find name, contact, education, and skills.

• Extracts metadata, skills, experience, education

• Categorizes technical stacks: Python, SQL, Cloud, ML, etc.

2️⃣ Skill Extraction & Embedding

• Extracted skills are converted to vector embeddings.

• FAISS is used to create a searchable vector store of job-role keywords.

3️⃣ Real-Time Job Search via SerpAPI

• Platforms supported: LinkedIn, Indeed, Glassdoor, Monster.

• Query formation uses extracted keywords and location preferences.

  Resume Analysis

  • Evaluates strengths and gaps

  • Suggests improvements (ATS keywords, skill alignment, structure)

  • Output is styled and summarized for user review

4️⃣ Job-Resume Matching

• Computes cosine similarity between job descriptions and the embedded resume.

• Matches are ranked and displayed with job details, posting source, and skill overlap.

  Job Match Score

  • Evaluates job descriptions against your resume

  • FAISS is used to embed and compare resume-job vectors

  • Returns match score, key skill overlaps, gaps, and actionables

5️⃣ Interview Questions Generation (LLM)

• Based on the matched job description and resume.

• Prompts include role, difficulty level, experience, and skills.

• Uses GPT to dynamically generate STAR-based or technical questions.

• Each question includes:

  • 📌 Context

  • 🧠 What it evaluates

  • ✅ Tips to answer

  • 📋 STAR-format example responses

🧑‍💼 Example Use Case:

Arpit uploads his resume, selects “Data Scientist” in “Bangalore,” and the system returns:

• ✅ 5 matched jobs from Indeed + LinkedIn

• 📊 Resume Score: 83%

• 🎯 Match Analysis:

  • Key Matches: Python, Pandas, Streamlit, ML

  • Gaps: No mention of Docker, Cloud deployments

• 🤖 Top 10 Interview Questions with contextual help

---

🧪 Key Features

• ✅ End-to-End Automation

• ✅ ATS-Optimized Resume Feedback

• ✅ Real-Time Job Listings with Relevancy Ranking

• ✅ Interview Preparation Mode (Behavioral + Technical)

• ✅ Modular CrewAI Agents

• ✅ Stylish Streamlit UI with multi-tab layout

  | Module | Purpose | | --- | --- | | resume_agent.py | Analyzes resume content, evaluates weaknesses, and provides suggestions | | job_search_agent.py | Extracts jobs from LinkedIn, Indeed, etc., using SERP API | | interview_agent.py | Generates tailored interview questions using GPT-3.5/4 | | app.py (UI) | Interactive Streamlit UI with upload, filters, insights, and results |

---

🚀 Real-World Use Cases

• 🎯 Personalized job hunting with 10x less manual effort

• 💡 Ideal for freshers and experienced professionals

• 🧠 Mock interviews and dynamic STAR question practice

• 👩‍💼 Resume analysis and improvement recommendations

---

🔄 Future Enhancements

• ✅ Agent Orchestration via CrewAI

• 🔐 Resume privacy layer & anonymization

• 🧾 Cover letter generator

• 🔄 Auto-job apply bot (with consent + CAPTCHA handling)

• 📧 Daily email digests with job + prep content

---

📂 GitHub & Local Setup

Repo: Job Search Assistant

🧪 Local Setup

git clone https://github.com/aarpitdubey/project.git
cd project/job/job_search_assistant
pip install -r requirements.txt
streamlit run app.py

You’ll see a dashboard with 4 tabs: Resume Analysis, Job Search, Interview Prep, Saved Jobs.

---

🎓 Conclusion

This project merges multiple AI paradigms—RAG, LLMs, semantic search, and agent orchestration—to build a practical tool for career growth. It demonstrates how Agentic AI systems can automate human-in-the-loop processes while maintaining context awareness, modularity, and real-time integration.

This Agentic AI system redefines job search using the latest LLM technologies. It acts like a virtual recruiter—analyzing, matching, and preparing you better than ever before. Perfect for modern professionals, career coaches, and even HR platforms looking to integrate smart AI hiring assistants.

Whether you're an engineer, data scientist, or product manager, this assistant can serve as your personal AI job hunter.

Ready to land your next job? Let your AI agent do the searching.