NexusMed AI: Advanced Multi-Agent Clinical Research Platform - 15/08/2026 05:13 EDT
Budget / Salary₹1,500–12,500
TypeFreelance project
LocationRemote
Posted2 hours ago
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.
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.
Apply on Freelancer →
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