A solo developer built JobSync, an AI-powered recruitment platform that uses dual-encoder semantic retrieval with transformer embeddings to match job descriptions and candidate profiles based on semantic similarity rather than keywords. The system converts both jobs and profiles into vector embeddings and retrieves candidates using vector search, improving matching accuracy for related roles with different terminology. The platform was built with FastAPI, Qdrant, PostgreSQL + pgvector, MongoDB, Redis, and Sentence Transformers, and deployed on CPU-only cloud infrastructure. The developer benchmarked vector databases, finding Qdrant faster for repeated semantic queries, and implemented remote LoRA fine-tuning via an external API to adapt models without local GPUs. Engineering challenges included dependency conflicts, async architecture, deployment reliability, model loading, cold starts, and latency/resource balancing, addressed with lazy loading, caching, and modular API routing. This project demonstrates practical, production-grade AI infrastructure for semantic job matching built by an independent developer using open-source tools and smart design.
Use Case
Opening the operator briefing
Pulling the full operator breakdown, tooling context, and verification notes.