From Zero to AI-Powered Job Assistant: How I Built JobQuest AI in 48 Hours with Autogen and Selenium

Just two days ago, terms like "Autogen" and "headless browser automation" were foreign concepts to me. Today, I'm proud to share JobQuest AI - an intelligent job-hunting assistant that completely transforms how professionals search for opportunities. Here's my journey from complete beginner to functional AI product. The Inspiration: Frustration Leads to Innovation As someone who recently went through the exhausting job search process, I experienced firsthand how inefficient traditional methods are: Endlessly scrolling through job boards Manually tailoring each application Guessing at salary expectations Preparing uniquely for every interview I realized: What if AI could handle 80% of this grind? That spark led me down a 48-hour coding marathon. Day 1: Diving Into the Unknown My first challenge was understanding Autogen, Microsoft's framework for creating collaborative AI agents. After reading documentation and experimenting: I built a "Searcher Agent" to understand job requirements Created a "Writer Agent" specialized for cover letters Developed a "Salary Analyst" using Gemini's market data Implemented conversation flows between agents Key breakthrough: Autogen's ability to have agents debate and refine outputs before presenting to users resulted in significantly higher quality suggestions. Day 2: The Web Scraping Challenge When I hit API limitations, I turned to Selenium: Mastered headless browser automation Built LinkedIn job scrapers that mimic human browsing patterns Implemented intelligent pagination handling Added proxy rotation to avoid detection The most satisfying moment? Watching the scraper perfectly extract and categorize 50+ job listings in under a minute. The Complete Tech Stack Core Framework: Python 3.11 (for stability and async support) Streamlit (for the clean, interactive dashboard) Autogen (multi-agent orchestration) AI Components: Groq API (ultra-fast LLM inference) Google Gemini (for salary/insight generation) Custom fine-tuned prompts (to maintain professional tone) Data Pipeline: Selenium WebDriver (headless Chrome) BeautifulSoup4 (HTML parsing) Custom NLP filters (for skill matching) Utility Layer: Python-dotenv (secure credential management) Loguru (beautiful logging) Pandas (data organization) Key Features That Make It Powerful Smart Job Matching Analyzes 100+ listings in minutes Scores matches based on your profile Highlights best-fit opportunities AI-Powered Cover Letters Generates tailored drafts in 15 seconds Maintains consistent professional voice Incorporates key job requirements Interview Preparation Suite Technical question predictions Company-specific advice Behavioral question drills Salary Negotiation Assistant Real-time market benchmarks Customized compensation analysis Benefits package evaluation The Development Challenges Rate Limiting: Solved with intelligent request throttling and cacheing Dynamic Web Content: Overcame with hybrid CSS/XPath selectors AI Hallucinations: Implemented a verification layer using consensus scoring Why This Changes Job Hunting Traditional job search tools are passive. JobQuest AI is: Proactive (searches for you) Adaptive (learns from your preferences) Comprehensive (handles the entire application cycle) What's Next? Adding Indeed and Glassdoor integration Developing a Chrome extension Implementing PDF resume parsing Final Thoughts This 48-hour sprint taught me that: Modern AI tools can automate complex workflows Web scraping remains an essential skill The best solutions come from personal pain points Try JobQuest AI Today: https://github.com/Zedoman/Job_Hunt

Apr 7, 2025 - 08:38

From Zero to AI-Powered Job Assistant: How I Built JobQuest AI in 48 Hours with Autogen and Selenium

Just two days ago, terms like "Autogen" and "headless browser automation" were foreign concepts to me. Today, I'm proud to share JobQuest AI - an intelligent job-hunting assistant that completely transforms how professionals search for opportunities. Here's my journey from complete beginner to functional AI product.

The Inspiration: Frustration Leads to Innovation
As someone who recently went through the exhausting job search process, I experienced firsthand how inefficient traditional methods are:

Endlessly scrolling through job boards

Manually tailoring each application

Guessing at salary expectations

Preparing uniquely for every interview

I realized: What if AI could handle 80% of this grind? That spark led me down a 48-hour coding marathon.

Day 1: Diving Into the Unknown
My first challenge was understanding Autogen, Microsoft's framework for creating collaborative AI agents. After reading documentation and experimenting:

I built a "Searcher Agent" to understand job requirements

Created a "Writer Agent" specialized for cover letters

Developed a "Salary Analyst" using Gemini's market data

Implemented conversation flows between agents

Key breakthrough: Autogen's ability to have agents debate and refine outputs before presenting to users resulted in significantly higher quality suggestions.

Day 2: The Web Scraping Challenge
When I hit API limitations, I turned to Selenium:

Mastered headless browser automation

Built LinkedIn job scrapers that mimic human browsing patterns

Implemented intelligent pagination handling

Added proxy rotation to avoid detection

The most satisfying moment? Watching the scraper perfectly extract and categorize 50+ job listings in under a minute.

The Complete Tech Stack
Core Framework:

Python 3.11 (for stability and async support)

Streamlit (for the clean, interactive dashboard)

Autogen (multi-agent orchestration)

AI Components:

Groq API (ultra-fast LLM inference)

Google Gemini (for salary/insight generation)

Custom fine-tuned prompts (to maintain professional tone)

Data Pipeline:

Selenium WebDriver (headless Chrome)

BeautifulSoup4 (HTML parsing)

Custom NLP filters (for skill matching)

Utility Layer:

Python-dotenv (secure credential management)

Loguru (beautiful logging)

Pandas (data organization)

Key Features That Make It Powerful

Smart Job Matching

Analyzes 100+ listings in minutes

Scores matches based on your profile

Highlights best-fit opportunities

AI-Powered Cover Letters

Generates tailored drafts in 15 seconds

Maintains consistent professional voice

Incorporates key job requirements

Interview Preparation Suite

Technical question predictions

Company-specific advice

Behavioral question drills

Salary Negotiation Assistant

Real-time market benchmarks

Customized compensation analysis

Benefits package evaluation

The Development Challenges
Rate Limiting: Solved with intelligent request throttling and cacheing

Dynamic Web Content: Overcame with hybrid CSS/XPath selectors

AI Hallucinations: Implemented a verification layer using consensus scoring

Why This Changes Job Hunting
Traditional job search tools are passive. JobQuest AI is:

Proactive (searches for you)

Adaptive (learns from your preferences)

Comprehensive (handles the entire application cycle)

What's Next?
Adding Indeed and Glassdoor integration

Developing a Chrome extension

Implementing PDF resume parsing

Final Thoughts
This 48-hour sprint taught me that:

Modern AI tools can automate complex workflows

Web scraping remains an essential skill

The best solutions come from personal pain points

Try JobQuest AI Today:
https://github.com/Zedoman/Job_Hunt