Most job platforms still rely heavily on keyword matching.

That means a candidate searching for “backend engineer” might never match with a company looking for a “server-side developer” — even though they’re essentially the same role.

I wanted to solve that problem.

So I built an AI-powered recruitment infrastructure called JobSync : a semantic matching system that understands meaning instead of just keywords.

What I Built

The platform uses a dual-encoder semantic retrieval architecture powered by transformer embeddings.

Instead of matching exact words, both job descriptions and candidate profiles are converted into vector embeddings, allowing the system to retrieve candidates based on semantic similarity.

For example:

“Python developer”

“Django engineer”

“Backend API specialist”

can all be recognized as closely related concepts.

The system was built with:

FastAPI

Qdrant

PostgreSQL + pgvector

MongoDB

Redis

Sentence Transformers

Docker

Async Python architecture

Why This Project Was…