Job Hunter AI: Next-Gen Job Search with n8n Agent and Amazon Bedrock

This is a submission for the AI Agents Challenge powered by n8n and Bright Data

What I Built

Job Hunter AI is a revolutionary job search automation workflow that combines the power of Amazon Bedrock Chat Model with n8n AI Agent Node and Amazon Nova Premier - AWS's latest multimodal AI model. This cutting-edge system automatically:

🔍 Scrapes job listings from LinkedIn using BrightData's verified nodes
🧠 Extracts recruiter contacts using Nova Premier's advanced reasoning capabilities

📝 Generates personalized cover letters with Amazon Bedrock agent framework
📊 Organizes everything in a comprehensive Google Sheets dashboard
🎯 Provides actionable insights with multimodal AI analysis

Revolutionary Features:

• Next-gen AI: Amazon Nova Premier (us.amazon.nova-premier-v1:0) via Amazon Bedrock Chat Model
• Advanced reasoning: Superior contact extraction and pattern recognition
• Tool capabilities: Native function calling through n8n AI Agent Node framework
• Multimodal processing: Handles text, images, and structured data
• Modern architecture: Clean Amazon Bedrock integration with n8n AI Agent nodes

n8n Workflow

GitHub Gist: Job Hunter AI Workflow JSON

The workflow is accessible through n8n's interface and can be triggered manually or scheduled to run automatically. Here's how it works:

1. Input your job search criteria (title, location, experience level)
2. Automated scraping begins on LinkedIn
3. AI analysis extracts key details including recruiter contacts
4. Personalized content generation for each job opportunity
5. Google Sheets export with all data organized and ready for action

Actual Google Sheets Exported Data:

⚠️ Security Note: All sensitive information has been removed from the workflow file:

• Google Sheet ID replaced with YOUR_GOOGLE_SHEET_ID
• BrightData credential ID replaced with YOUR_BRIGHTDATA_CREDENTIAL_ID
• AWS credential ID replaced with YOUR_AWS_CREDENTIAL_ID
• Google Sheets credential ID replaced with YOUR_GOOGLE_SHEETS_CREDENTIAL_ID

⚠️ Security Best Practice: Always use temporary AWS credentials with least privilege permissions when testing your n8n Agent with AWS services. Never use root credentials or long-term access keys in development environments. Users must configure their own credentials and Sheet ID before importing the workflow.

Technical Implementation

System Architecture

The workflow follows a sophisticated data pipeline:

Input Layer → Scraping Layer → AI Processing → Output Layer

{ "name": "Job Hunter AI - LinkedIn Automation", "nodes": [ // Complete workflow with 15+ nodes including: // - Manual Trigger // - BrightData scrapers for LinkedIn // - AI Agent for data extraction // - Amazon Nova Premier for cover letter generation // - Google Sheets integration // - Data normalization and structuring ] } 

n8n AI Agent Configuration

System Instructions:

You are an expert recruiter and data extraction specialist. Analyze job postings and extract structured information with high precision. Always return valid JSON only. 

Prompt:

Analyze this job posting and extract detailed information: Job Title: {{ $json.jobTitle }} Company: {{ $json.companyName }} Location: {{ $json.jobLocation }} Description: {{ $json.description }} Salary: {{ $json.salary }} Source: {{ $json.sourcePlatform }} URL: {{ $json.jobUrl }} Extract and return ONLY valid JSON with these exact fields: { "companyName": "exact company name", "jobTitle": "exact job title", "description": "cleaned job description", "salaryRange": "salary information or 'Not specified'", "location": "job location", "workType": "Remote/Hybrid/Onsite/Unknown", "applicationDeadline": "deadline or 'Not specified'", "recruiterEmail": "email if found or 'Not found'", "hiringManagerEmail": "email if found or 'Not found'", "recruiterLinkedIn": "LinkedIn profile if found or 'Not found'", "benefits": ["benefit1", "benefit2"], "keySkills": ["skill1", "skill2"], "experienceLevel": "Entry/Mid/Senior/Executive", "jobUrl": "{{ $json.jobUrl }}", "sourcePlatform": "{{ $json.sourcePlatform }}", "extractedAt": "{{ new Date().toISOString() }}" } 

Model Choice: Amazon Nova Premier

• Reasoning: Superior performance in structured data extraction and natural language generation
• Context window: Handles large job descriptions effectively
• Multilingual support: Works with international job postings

Memory: Stateless execution with job criteria persistence across workflow steps

Tools Used:

• BrightData Community Node: Professional web scraping via n8n-nodes-brightdata
• Amazon Nova Premier: Latest multimodal AI via Amazon Bedrock integration
• Google Sheets API: Data storage and organization
• n8n Code Nodes: Data transformation and normalization

Bright Data Verified Node

The BrightData Verified Node is the backbone of this workflow, providing:

Reliable Data Collection:

• LinkedIn Jobs Scraper: Extracts job listings, company details, and posting metadata
• Anti-detection technology: Ensures consistent data collection without blocks
• Structured data output: Clean, normalized job data ready for AI processing

Bright Data Account Proxy Infrastructure Configuration

Bright Data Verified Node Implementation Details:

 // LinkedIn scraping configuration { "parameters": { "zone": { "__rl": true, "value": "mcp_unlocker", "mode": "list", "cachedResultName": "mcp_unlocker" }, "country": { "__rl": true, "mode": "list", "value": "us" }, "url": "=https://www.linkedin.com/jobs/search/?keywords={{ encodeURIComponent($json.jobTitle) }}&location={{ encodeURIComponent($json.location) }}&f_TPR=r86400", "format": "json", "requestOptions": {} }, "id": "97295b11-8c10-48c2-9215-9fb413e1c253", "name": "LinkedIn Web Unlocker", "type": "@brightdata/n8n-nodes-brightdata.brightData", "position": [ -1952, -272 ], "typeVersion": 1, "credentials": { "brightdataApi": { "id": "YOUR_BRIGHTDATA_CREDENTIAL_ID", "name": "BrightData account" } } } 

Data Quality Assurance:

• Duplicate detection: Prevents duplicate job entries across platforms
• Data validation: Ensures all required fields are populated
• Error handling: Graceful fallbacks when scraping encounters issues
• Rate limiting: Respects platform guidelines while maximizing data collection

Journey

Development Process

Phase 1: Research & Planning

• Analyzed existing job search tools and identified pain points
• Researched BrightData capabilities and n8n integration patterns
• Designed workflow architecture and data flow

Phase 2: Core Implementation

• Built initial scraping workflow for LinkedIn
• Integrated BrightData Verified Node with proper error handling
• Developed data normalization and cleaning processes

Phase 3: Next-Gen AI Integration

• Implemented Amazon Bedrock with Amazon Nova Premier model
• Integrated native n8n Bedrock nodes for superior reasoning capabilities
• Created sophisticated prompts leveraging multimodal AI features
• Built advanced cover letter generation with structured output

Phase 4: Polish & Testing

• Added Google Sheets integration for comprehensive tracking
• Implemented skills gap analysis and interview preparation features
• Extensive testing across different job types and locations

Challenges Overcome

1. Data Consistency Optimization

• Challenge: LinkedIn job data required careful parsing and normalization
• Solution: Built robust normalization layer that handles various data formats
• Learning: Always plan for data variability in web scraping projects

2. Recruiter Contact Extraction

• Challenge: Contact information is often hidden or requires inference
• Solution: Used AI to analyze job descriptions, company pages, and posting patterns
• Learning: LLMs excel at pattern recognition in unstructured text

3. Next-Gen AI Architecture

• Challenge: Traditional AI integrations lack modern capabilities
• Solution: Used Amazon Bedrock Chat Model with n8n AI Agent Node for cutting-edge performance
• Learning: Modern AI frameworks provide superior developer experience and results

4. Cover Letter Personalization

• Challenge: Generic templates don't stand out to recruiters
• Solution: AI analyzes both job requirements and user resume for perfect matching
• Learning: Context-aware AI generation significantly improves output quality

What I Learned

Technical Insights:

• BrightData Community Node provides reliable, scalable web scraping
• Amazon Nova Premier delivers state-of-the-art reasoning capabilities
• Amazon Bedrock integration enables native n8n AI workflows
• Multimodal AI opens new possibilities for job data analysis
• Modern architecture improves maintainability and performance

Product Insights:

• Job seekers need more than just job listings - they need actionable intelligence
• Personalization at scale is possible with the right AI tools
• Contact information is often the missing piece in job applications
• Comprehensive tracking helps job seekers stay organized and strategic

Business Insights:

• Time-to-application is critical in competitive job markets
• Quality over quantity - better to apply to fewer jobs with personalized materials
• Data-driven job searching provides significant competitive advantages
• Automation frees up time for networking and skill development

Future Enhancements

Planned Features:

• Salary negotiation insights based on market data
• Company culture analysis from employee reviews
• Application tracking with follow-up reminders
• Interview scheduling automation
• Skills recommendation engine based on job market trends

Technical Improvements:

• Real-time notifications for new matching jobs
• Mobile app integration for on-the-go job management
• Advanced filtering with ML-based job matching
• Integration with ATS systems for direct application submission

Built with ❤️ using n8n and BrightData

Transforming job search from a tedious process into an intelligent, automated system that helps candidates find their dream roles faster and more effectively.