This is a submission for the Bright Data Web Scraping Challenge: Scrape Data from Complex, Interactive Websites, Build a Web Scraper API to Solve Business Problems and Most Creative Use of Web Data for AI Models (All 3 prompts).
What I Built
In today’s fast-paced job market, staying ahead requires access to real-time, accurate, and comprehensive data. Traditional job boards often fall short of providing users with the depth of insights they need to make informed career decisions.
Current Problem
Imagine this: You wake up, grab your coffee, and brace yourself for the daily grind of job hunting. Logging into 10 different websites, typing in job roles repeatedly, applying countless filters, and scrolling through overwhelming lists of jobs—it’s exhausting, inefficient, and frustrating.
Solution
You log in to the platform, upload your resume, and that’s it! You’re greeted with a personalized dashboard featuring AI tailored job recommendations and the freshest roles daily, handpicked for you. No filters, no chaos—just opportunities that match your skills, preferences, and aspirations.
The platform transforms this daily struggle into a seamless experience, saving you time, effort, and stress. Here’s how we’ve combined cutting-edge AI and Bright Data’s Web Scraper API to revolutionize job hunting.
Key Features
1. AI-Powered Job Recommendations
- Matches users to roles based on their resumes, profiles, and preferences
- Highlights skill gaps and recommends learning resources
2. Conversational Job Search
- An intuitive chatbot allows users to search for roles using natural language queries, such as "Find remote Python developer jobs paying ₹80,000+"
- Provides job comparisons, tailored resumes, and cover letters
3. Personalized Dashboard
- Tracks saved jobs, applications, and sends alerts for matching roles
4. Interview Preparation
- AI generates likely interview questions tailored to specific roles
- Provides suggested responses and feedback for improvement
Demo
Experience the platform in action:
- Live Demo: https://jobscout-ai.vercel.app
- Github Link: https://github.com/abhinav-m22/JobScout.ai
- Demo Video:
- Screenshots:
Dashboard
AI Assistant
Architecture
The system combines Bright Data’s Web Scraper API with cutting-edge AI to deliver a seamless user experience.
The Platform Workflow
1. Data Collection
- Bright Data's Web Scraper API fetches job data from targeted platforms
2. Data Delivery to Cloud Storage
- Scraped data is stored in JSON format on AWS S3
3. AI Processing
- AI models analyze data to generate personalized recommendations and insights
4. User Interaction
- Users interact with the platform through an intuitive dashboard, chatbot, and recommendation engine
5. Continuous Updates
- Periodic background tasks ensure data freshness and relevance
How I Used Bright Data
Bright Data's Web Scraper API is the backbone of this platform. Here's how it's utilized:
1. Scraping Job Data
- Scrapes job listings from platforms like LinkedIn, Glassdoor, and Indeed using company URLs or keywords
- Captures crucial details such as:
- Job titles
- Descriptions
- Required skills
- Salaries
- Benefits
- Locations
- Hiring trends
- Ensures real-time updates for the latest job opportunities
2. Snapshot Creation and Delivery
- Background tasks periodically hit Bright Data APIs to fetch job roles based on AI-determined roles, locations, job types, and time ranges
- Each data collection generates a snapshot ID, which allows efficient data storage and reference job roles with their corresponding IDs.
- JSON responses from Bright Data API are directly saved to AWS S3 for efficient data handling with the help of Bright Data's Delivery API
Qualifying for Additional Prompts
Prompt 1: The platform efficiently scrapes complex, interactive sites like LinkedIn, Glassdoor and Indeed.
Prompt 2: Offers APIs for personalized job recommendations and role matching.
Prompt 3: Incorporates AI for job recommendations, conversational search, sentiment analysis, and interview preparation.
Top comments (2)
Nice Project. I wish the backend was in Node.