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. An n8n automation workflow template by Navneet Singh Arora.
Navneet Singh Arora
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