Senior AI / LLM Engineer (RAG, OpenAI, Vector Search)

We're looking for an experienced AI / LLM Engineer to design and build the first demo of M.A.R.E. XO — an AI-powered maritime intelligence assistant integrated into our existing platform. This is not a generic chatbot. The assistant must answer questions using only trusted, controlled sources such as platform data, uploaded documents, vessel information, geofence data, incident records, and exposure analytics. Responses should include citations to the original sources, minimize hallucinations, and support analyst review when needed. The scope includes designing the RAG architecture, implementing document ingestion and vector search, defining chunking and embedding strategies, creating prompt workflows, establishing AI guardrails, optimizing token usage and API costs, and collaborating with our backend/frontend team to integrate the AI layer into the existing product. We're looking for someone with strong hands-on experience building production-ready RAG systems using technologies such as OpenAI, Azure OpenAI, Anthropic, or Gemini, along with experience in embeddings, vector databases, prompt engineering, and backend integration (Node.js/TypeScript preferred). If you've built reliable AI assistants that retrieve information from structured and unstructured data with source-linked answers, we'd love to hear from you. Please share examples of similar projects, the AI stack you've used, and your recommendations for this solution.