AI-Powered Job Search Automation: Building an End-to-End Job Hunting Pipeline with n8n and LLM

Title: AI-Powered Job Search Automation: Building an End-to-End Job Hunting Pipeline with n8n and LLM

Project developed by fay-cloud (open-source on GitHub, released on June 15, 2026), built on n8n to create an automated job search system. It scrapes Indeed data engineer positions via Apify, uses LLM to assess matching degree and generate customized cover letters, stores data in Google Sheets for tracking, achieves end-to-end automation, reduces manual work, and allows job seekers to focus on interview preparation and career planning.

Manual job search processes are repetitive and time-consuming: it takes hours to search for positions across multiple platforms, assess matching degrees, write cover letters, and track progress, often leading to missed opportunities or delayed follow-ups. With the maturity of AI (especially large language models), tedious processes can be automated, freeing job seekers' energy for high-value tasks.

Project Overview

Targeting Indeed data engineer positions in the Netherlands, it integrates workflow automation, web scraping, LLM, and cloud tracking to achieve end-to-end automation from job discovery to application tracking, with the core being AI matching assessment + personalized cover letter generation.

Core Features

• Automatically scrape Indeed positions
• AI resume matching scoring
• Intelligent language detection (Dutch)
• Custom cover letter generation (ATS-compliant)
• Centralized tracking via Google Sheets
• Duplicate detection
• Scheduled automatic execution

Technical Architecture

Pipeline design: Scheduled trigger → Apify scraping → Dataset acquisition → LLM resume assessment → LLM cover letter generation → Google Sheets storage

Tech Stack

Category	Technology
Workflow Orchestration	n8n
Job Scraping	Apify Indeed Scraper
AI Model	OpenAI GPT-5 Mini
Data Storage	Google Sheets
Scheduling	n8n Schedule Trigger
Prompt Engineering	Custom LLM prompts
Automation Platform	n8n Cloud

Practical tech stack selection: Low-code n8n lowers the barrier, Apify handles scraping, Google Sheets for lightweight storage, and GPT-5 Mini balances cost and performance.

The system significantly reduces manual work:

• Automatically discovers opportunities without manual browsing
• AI automatically filters matching degrees, eliminating manual screening
• Scalable generation of personalized cover letters
• Structured application tracking via Google Sheets
• Daily unattended execution

Data engineer job seekers can turn hours of daily mechanical work into a few minutes of system monitoring.

Key Insights

• AI Agents do not replace human decision-making; instead, they automate repetitive tasks, allowing humans to focus on high-value judgment and creation
• The combination of low-code platforms (n8n) and AI services is powerful: quickly build systems that would take weeks with traditional development
• Automation is both an efficiency tool and a mental burden reliever, reducing job search stress

Conclusion

AI-powered job search automation represents a new direction for personal productivity tools. As LLM costs decrease and usability improves, more similar systems will emerge. This open-source project provides developers with a practical starting point for building job search assistants.

Project future plans:

• Data source expansion: Integrate LinkedIn Jobs
• Automated submission: Explore auto-filling forms
• Multi-candidate support
• Intelligent notifications: Email alerts for high-matching positions
• Data storage upgrade: PostgreSQL/Snowflake
• Visual dashboard: Power BI
• Company research automation
• Multi-country support
• Intelligent threshold filtering: Filter positions by score to optimize costs