LLM Engineer for Real Estate AI Development
Budget: $750 – $1,500 USD
Got it — here’s a much shorter, copy-and-paste **Freelancer.com listing** for your **LLM Engineer** role:
---
**Title:** LLM Engineer – Build RAG Chat & AI Summaries for Real Estate Tool
**Description:**
We’re building a real estate intelligence platform and need an **LLM Engineer** to add AI features on top of our database. You will:
* Build a **chat assistant (RAG)** that answers queries using SQL + vector search with citations.
* Create **grounded summaries** of deals and reports (no hallucinations).
* Develop **predictive models v1** (distress / rent growth) with explainability.
**Skills Needed:**
* Experience building **RAG systems** (SQL + vector DBs like pgvector, Pinecone, or Weaviate).
* **Prompt engineering** and JSON schema enforcement.
* Python (FastAPI), Postgres/SQL, embeddings, caching.
* Machine learning (XGBoost/GBM, SHAP or LIME).
* Bonus: Real estate/finance data experience.
**Deliverables:**
* Summaries + CRM notes
* Chat API with citations
* Predictive scoring model with explainable drivers
**Timeline:** \~6–8 weeks (milestone based).
**Budget:** Fixed price per milestone.
**To Apply:** Start your bid with **“LLM READY”** and include:
1. Example of a RAG/chat system you built.
2. How you prevent hallucinations.
3. Your proposed milestone costs.
---
**Title:** LLM Engineer – Build RAG Chat & AI Summaries for Real Estate Tool
**Description:**
We’re building a real estate intelligence platform and need an **LLM Engineer** to add AI features on top of our database. You will:
* Build a **chat assistant (RAG)** that answers queries using SQL + vector search with citations.
* Create **grounded summaries** of deals and reports (no hallucinations).
* Develop **predictive models v1** (distress / rent growth) with explainability.
**Skills Needed:**
* Experience building **RAG systems** (SQL + vector DBs like pgvector, Pinecone, or Weaviate).
* **Prompt engineering** and JSON schema enforcement.
* Python (FastAPI), Postgres/SQL, embeddings, caching.
* Machine learning (XGBoost/GBM, SHAP or LIME).
* Bonus: Real estate/finance data experience.
**Deliverables:**
* Summaries + CRM notes
* Chat API with citations
* Predictive scoring model with explainable drivers
**Timeline:** \~6–8 weeks (milestone based).
**Budget:** Fixed price per milestone.
**To Apply:** Start your bid with **“LLM READY”** and include:
1. Example of a RAG/chat system you built.
2. How you prevent hallucinations.
3. Your proposed milestone costs.
Related categories:
Python
SQL
MySQL
PostgreSQL
Database Development
Predictive Analytics
Prompt Engineering
LLM Integration