I have developed a high-end foundation for NexusMed AI, a multi-agent clinical research platform, and I am looking for a senior AI Engineer to lead the next phase of development. The project currently has a stunning "Blue-Green-White" professional UI and a robust Deno-based backend utilising LangGraph for complex agent orchestration. Key Objective: The primary goal is to finalize and enhance the specialized Literature Review Agent. This agent must move beyond general web search to perform deep, evidence-based research for medical professionals. Specific Tasks: Deep Database Integration: Implement robust integrations with PubMed (via Entrez API) and Google Scholar to retrieve peer-reviewed medical journals and clinical trials. Advanced RAG Implementation: Enhance the Retrieval-Augmented Generation (RAG) system to ensure all AI claims are cross-referenced with real, clickable citations from the integrated databases. Real-time Streaming: Polish the WebSocket stream so that research synthesis appears live in the frontend panels. Biomedical Reflection: Refine the "Reflection Agent" (currently using a fine-tuned biomedical Llama model) to perform a secondary quality check on the literature review output. Technical Stack: Backend: Deno v2 (TypeScript), WebSockets. Frontend: React, Vite, Tailwind CSS, Framer Motion. AI Logic: LangGraph, LangChain. Models: Groq (Llama 3.3 70B Orchestrator) & Local Ollama (Biomedical fine-tuned SLM). What’s Already Done: ✅ Stable Multi-agent state graph and orchestration logic. ✅ Professional, responsive dashboard with glassmorphism effects. ✅ Backend/Frontend communication via WebSockets. I am looking for a developer who understands medical data sensitivity and has deep experience in Agentic RAG and Academic API integrations.