Resume Processing and Search Pipeline

Requirements Clarification & Assessment

1. Purpose of the Tool

   • The primary goal is to convert resumes into searchable text data to facilitate internal analytics and enhance the recruitment process.
   • The tool should support both machine learning (ML) models for natural language processing (NLP) and keyword monitoring for company analysts.

2. Key Outcomes

   • Data Mart Creation: A repository for ML models to access processed text data.
   • Data Product Development: A tool for analysts to monitor specific keywords within resumes.
   • Search API Implementation: An interface for recruiters to perform keyword searches on processed resumes.

3. Assumptions

   • Image-to-text (I2T) conversion models are pre-existing and reliable.
   • The system does not require real-time processing but should ensure swift data availability.
   • Privacy and security measures are managed by a separate team.
   • Scalability requirement: handling up to 1000 images per 24 hours.
   • Cloud-based infrastructure is preferred for high availability.

4. Clarifying Questions

   • What specific insights are desired from the analytics?
   • Are there particular regions or formats of resumes that need special handling?
   • What are the key performance indicators (KPIs) to measure the tool's success?