AI/Backend Developer

We are looking for an experienced AI/Backend Developer to build an internal AI knowledge platform integrating Local LLMs, Box, and multiple external AI services.

The goal is to provide 10–20 employees with a single internal interface where they can search and utilize company knowledge stored in Box, interact with local and external AI models, and generate answers and documents with clear source references.

Key requirements include:

- Local LLM deployment on Mac Studio (Ollama or similar)
- Box API integration and on-demand search across large-scale company data
- RAG, document retrieval, and reranking
- Integration with external AI APIs such as OpenAI/GPT, Gemini, Grok, etc.
- AI Router/Orchestrator for selecting or combining multiple models
- User authentication and permission-aware document access
- Conversation history, knowledge management, and audit logs
- API usage/cost tracking and security controls
- Backend/API architecture suitable for future expansion

We do not plan to download or embed the entire Box dataset. The preferred approach is to search Box on demand, retrieve only relevant documents, process/rerank them, and provide the necessary context to the selected LLM.

We would like to begin with a small PoC and expand to production after validating search accuracy, response quality, performance, security, and operating costs.

When applying, please briefly describe your experience with Local LLMs, RAG, Box or similar enterprise storage APIs, multi-LLM integration, and the technology stack you would recommend for this project.

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