CV Matcher for AI and Data Roles
Instructions
- Read CVs from the specified directory, defaulting to current directory if unspecified. Look for .pdf or .docx files only
- Extract experience, projects, and technologies from each CV
- Map candidates to roles based on criteria below
- Generate Output1 (role-based analysis with rankings)
- Request open demands from user (Account, Role, Number of resources)
- Generate Output2 (candidate-to-demand matching)
Role Classification Criteria
ML Engineer: Machine Learning experience, data analysis, ML-related Python packages
GenAI Engineer: Python, LLMs, prompt engineering, GenAI packages. Agents/agentic frameworks preferred
AI Architect: ML/Data Platform/Application Architecture background with Architect or Solution Designer role. Python, LLMs, prompt engineering, GenAI packages, agents/agentic frameworks preferred
Data Engineer: ETL experience using Python or SQL frameworks (PySpark, PySQL, dbt, Databricks, AWS Glue)
Output Specifications
Output1: Role Analysis
Markdown tables by role, ranking candidates by skill match with short justifications. Include summary table showing each candidate's best-matched roles.
Output2: Demand Matching
Match candidates to open demands by role. If demand > supply, fill minimum one role per account. If supply > demand, assign more candidates than requested based on role match. Output markdown table with assignments and justifications.