Backend Engineer for RAG in Healthcare Setting

Backend Engineer Needed for RAG Architecture (LLM + Vector Database)


---

Project Description

I am looking for an experienced developer to design and implement a production-grade RAG (Retrieval-Augmented Generation) backend for a controlled knowledge system.

This is not a generic chatbot project. The goal is to build a system where a language model can query a structured knowledge base while enforcing strict safety and retrieval rules.

The system will be used in a clinical / healthcare-related context, so accuracy, traceability and rule enforcement are critical.


---

Current Infrastructure

The current setup includes:

WordPress website hosted on Hostinger (content origin / source of truth)

Cloud environment on Hetzner available for backend deployment

Initial automation infrastructure already started


The WordPress site must remain separate from the AI backend. It acts only as the knowledge source, not as the execution environment for the AI system.


---

Objective

Build a RAG pipeline that allows an LLM to query a curated knowledge base while enforcing strict retrieval rules and preventing unsupported answers.


---

Key System Rules

The system must enforce the following rules:

1. No-retrieval = No-answer

If the system cannot retrieve relevant evidence from the knowledge base, the LLM must not generate an answer.

The pipeline should stop and return a controlled response instead.


---

2. Evidence-based responses

All responses must be grounded in retrieved source chunks.

The model must not:

invent information

infer protocols

generate details not present in the sources



---

3. Context separation

The system must prevent cross-context mixing between different topics or treatment types.

Retrieval should use semantic filtering and metadata constraints to ensure the correct context.


---

4. Traceability

Responses should allow traceability to:

source chunks

document IDs

content versioning when the website is updated



---

Expected Architecture

Content Origin
WordPress / Hostinger

RAG Backend

ingestion pipeline

semantic chunking

embeddings

vector database

retrieval + validation layer


LLM Layer
Invoked only after successful retrieval

Rules must be enforced outside the model, in the orchestration layer.


---

Infrastructure

Possible stack:

Python backend

Vector database (Pinecone, Weaviate, Qdrant, etc.)

Hetzner infrastructure

API layer for query handling


Content ingestion may come from:

controlled exports

PDFs

structured feeds

limited and controlled scraping if necessary



---

Deliverables

RAG backend architecture

ingestion pipeline

vector database configuration

retrieval and validation logic

API layer

deployment instructions

documentation



---

Screening Question

Please answer this question in your proposal:

How would you implement a strict “no-retrieval = no-answer” rule in a production RAG architecture?


---

Ideal Candidate

Experience building RAG systems

Experience with vector databases

Python backend development

Experience with LLM orchestration

Ability to design production architectures



---

Budget

Open to proposals depending on experience and scope.


---

Consejo importante

Cuando publiques en Freelancer:

selecciona AI / Machine Learning

selecciona Python

selecciona Backend Development


Eso hace que el proyecto llegue a ingenieros correctos y no solo a programadores web.