AI Recruitment & Candidate Matching Agent

Project Overview

AI Recruitment & Candidate Matching Agent (documented under 05_CV_Matching_RAG) is a dedicated 29-node workflow case study for high-precision semantic recruitment matching. Triggered via webhook or manual testing, the pipeline converts job requisitions into dense vector representations using OpenAI text-embedding-3-small. A first-stage vector query against Pinecone retrieves candidate profiles based on embedding similarity. Cohere's neural cross-encoder then re-ranks candidates to resolve nuanced contextual requirements, while OpenAI GPT-4 synthesizes structured score breakdowns and matching rationales explaining why each applicant fits the requisition.

Key Engineering Highlights

• 29-Node Automated Workflow: End-to-end orchestration in n8n (AI_Recruitment_Candidate_Matching.json)
• Dense Vector Embeddings: Encodes complex job requisitions with OpenAI text-embedding-3-small
• Pinecone Vector Retrieval: Queries candidate database using approximate nearest neighbors search
• Cohere Neural Re-Ranking: Evaluates cross-encoder context to boost precision over standard cosine distance
• AI Fit Explanations: Generates candidate match summaries with GPT-4 comparing candidate skills to job needs

Technology Stack