Semantic Search Engine Development

We are looking for a developer to design and implement a process that powers the backend of a semantic search engine. The goal is to collect text quotes, generate vector embeddings using OpenAI's embedding models, and store them in a vector database such as Qdrant. Once stored, the system should be able to use GPT-4 to further analyze and filter the most relevant quotes in response to natural language search queries.

The ideal candidate has hands-on experience with text processing and natural language understanding, as well as a strong grasp of working with vector databases and APIs. Familiarity with technologies such as OpenAI Embeddings, GPT-4, and semantic search workflows is essential. You should be comfortable integrating AI models into scalable systems and building clean, efficient APIs to support a web-based dashboard.

We’re seeking someone who brings not only technical expertise, but also a creative and structured approach to solving complex problems in AI-driven search and content recommendation. If that sounds like you, we’d love to hear from you.