RAG Integrated AI Resume Assistant Builder - FastAPI | RAG | Ollama

I'm looking for a developer to build a project that implements a Retrieval-Augmented Generation (RAG) pipeline enabling semantic search and conversational AI with a resume (CV).

The goal is to create a cost-effective architecture that runs the application logic locally while offloading heavy LLM inference to the cloud. The application should support semantic search over the resume and allow a conversational interface to answer questions about the resume content.

This project is focused on building the RAG pipeline, integrating the embeddings, and ensuring a secure connection between the local backend and cloud model server.

- Responsibilities
Design and implement the RAG pipeline for resume semantic search
Integrate embedding generation using HuggingFace models
Set up local backend using FastAPI
Connect to cloud LLM inference (Ollama) for conversation
Implement secure tunnel using ngrok
Debug and optimize the workflow
Document setup and usage

Project Details
New project build (not an existing application)
Focus on RAG architecture and secure local-cloud integration
No major UI or frontend work required

Requirements
Experience with FastAPI and Python
Strong understanding of RAG and semantic search
Experience with LangChain or similar frameworks
Familiarity with HuggingFace embeddings
Ability to configure ngrok and secure local endpoints
Clear communication and independent problem solving

If you’re excited about building cost-effective AI systems and RAG pipelines, I’d love to hear from you.