AI Resume Job Title Extractor with Google Gemini

AI Resume Job Title Extractor with Google Gemini

Overview

This n8n template demonstrates how to use Google Gemini AI to analyze a PDF resume and extract the candidate's current or most relevant job title as a clean search string. It’s a powerful utility for anyone building automated talent pipelines, lead enrichment tools, or profile categorization systems.

🚀 How it works

File Input: The workflow begins by reading a resume (PDF) from a local storage directory via the Read/Write Files from Disk node.

Parsing: The Extract from File node handles the heavy lifting of converting the PDF binary into indexed plain text. AI Analysis: The extracted text is sent to Google Gemini. A specialized prompt instructs the model to identify the professional role and output only a single, clean search string (avoiding any conversational filler).

Data Structuring: The Set (Return) node captures the AI's output and raw text, making it ready for downstream use or for return to a parent "caller" workflow.

🎮 How to use

File Path: Update the File Selector in the "Read/Write Files from Disk" node to point to your resume (e.g., /home/node/.n8n-files/My-Resume.pdf). API Credentials: Set up your Google Gemini (PaLM) API credentials in the Gemini node.

Modular Use: This workflow is configured with an Execute Workflow Trigger, meaning you can call it as a "sub-workflow" from larger automation stacks to instantly enrich data with professional titles.

⚙️ Requirements

Google Gemini API Key n8n Environment: Designed for self-hosted instances with local file access (can be adapted for Google Drive or S3 by swapping the first node).

🎯 Use Cases

Job Matching: Automatically generate search queries for job boards based on a user's resume.

CRM Enrichment: Keep your candidate lists up-to-date with current professional titles.

Talent Discovery: Categorize thousands of profiles instantly without manual review.