AI-based RAG, LLM, LangChain, LangGraph, ML Expert for Full-Stack Improvements

I am working on an AI-powered Retrieval-Augmented Generation (RAG) assistant built using LangChain, LangGraph, and LLMs, with a Python backend and a React frontend.
My current implementation has several minor but important issues affecting response accuracy and data retrieval flow within the LangChain/LangGraph pipeline. Additionally, there are a few frontend display inconsistencies that need immediate correction.

I urgently need a freelancer to:

Debug and improve specific parts of the RAG pipeline to enhance retrieval accuracy.

Correct small but essential issues in both the backend (Python) and frontend (React).

Ensure seamless interaction between the frontend UI and AI backend services.

- Required Skills:
LangChain / LangGraph: Good understanding of chains, agents, and graph structures.

Python (Backend): Experience with AI/ML development and LLM integration.

React (Frontend): Ability to troubleshoot and adjust React components.

ML Concepts: Comfortable working within ML-based pipelines and RAG architectures.

Strong debugging, optimization, and clean coding skills.

- Deliverables:
Accurate improvements to the AI RAG system’s retrieval logic.

Quick fixes to frontend issues affecting user interactions.

Clean, well-documented code updates.

Functional validation of changes.

- Budget:
$30 USD
(Scope limited to small, precise improvements — must be completed accurately and efficiently.)

- Ideal Candidate:
AI/ML developer familiar with LangChain, LangGraph, and LLMs.

Full-stack capable (Python + React).

Quick turnaround availability.

Detail-oriented and reliable communicator.

If you’re available and confident you can quickly resolve these urgent issues, please apply now — we’re ready to get started immediately.