Full custom engineering of an autonomous talent recruitment matching platform. Implemented advanced semantic vector database search filters and real-time applicant indexing pipelines.
The client was struggling with legacy keyword-based applicant matching systems. Recruiters spent hours manually parsing PDF resumes, resulting in slow placement cycles and high mismatch rates.
Building a high-throughput semantic search index that could accurately parse thousands of complex resumes and match them against diverse job descriptions in real-time, under 150ms.
We designed a decoupled architecture with a Next.js frontend and a FastAPI microservice. Resumes are ingested, OCR-parsed, embedded into high-dimensional vectors, and stored in a specialized vector database. The matching engine utilizes customized cosine similarity algorithms to recommend top candidates instantaneously.
User uploads resumes via client-side Next.js portal.
Asynchronous background workers parse and clean documents.
FastAPI service generates embedding vectors using state-of-the-art semantic models.
Vectors are indexed inside VectorDB with customized search filters.
Recruiters receive a sorted list of matches ranked by semantic relevance in under 150ms.
Discovery & Data Structuring: Mapped semantic entities and recruiter workflows.
Pipeline Development: Built the OCR extraction engine and vector embedding scripts.
API Integration: Developed FastAPI endpoints for search indexing and ranking filters.
Frontend Implementation: Designed the matching dashboard in Next.js.
Performance Tuning: Calibrated vector index parameters to optimize query times.
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