Senior RAG Engineer - Production SMS AI System (Real Estate) - 25k-35k
1 day ago
Job description
Real Estate SMS AI PlatformWe are building a production-scale AI-driven SMS lead nurturing platform for Australian real estate agencies. The system will manage 100,000+ contacts per agency using real-time data streams, advanced RAG pipelines, and conversational AI orchestration.
This is not an experimental build. We are hiring an experienced engineer who has already deployed high-accuracy RAG systems in real production environments.
Core Requirements
You must demonstrate:
- At least 3 production RAG deployments with verifiable examples
- Hands-on experience with purpose-built vector databases: Qdrant, Pinecone, or Weaviate
- Proven ability to achieve 95%+ answer accuracy and less than 5% hallucination rates
- Experience designing real-time event-driven systems (not batch ETL pipelines)
- Strong LLM engineering background including structured outputs, prompt optimization, and evaluation frameworks
- Python backend expertise using FastAPI or Flask with async processing and Docker
- Real-time messaging or SMS API integrations
Important:
Supabase or pgvector is not suitable for this project.
Technology Stack
Vector Database:
Qdrant or Pinecone
LLM:
OpenAI GPT-4 or Anthropic Claude
Frameworks:
LangChain or LlamaIndex
Backend:
Python with FastAPI
Integrations:
Twilio or similar SMS APIs, GoHighLevel CRM, real-time property data sources
Deployment:
Docker on AWS or GCP
Project Timeline
12 Week Delivery
Phase 1 — RAG Foundation (Weeks 1 to 4)
Build a multi-source retrieval pipeline with:
- CRM and property data ingestion
- Optimized vector database architecture
- Async embedding pipeline
- Hybrid retrieval combining semantic and keyword search
- Reranking layer to improve accuracy
Phase 2 — Messaging & Real-Time Integration (Weeks 5 to 8)
- SMS conversation workflows
- Conversational state management
- GoHighLevel CRM synchronization
- Real-time property API webhooks
- Message queue architecture using Redis or RabbitMQ
- Propensity modeling for buyer and seller intent
- Intent classification engine
- Next-best-action recommendation logic
- Campaign orchestration and scheduling
- A/B testing and analytics pipeline
- Production deployment using Docker
- Load testing and performance tuning
- Monitoring and alerting setup
- Full technical documentation
- Knowledge transfer session
- Two weeks post-launch support
Answer relevance above 95 percent
Hallucination rate below 5 percent
Response latency under 1 second
Support for 100k+ contacts per agency
Real-time campaign triggers within seconds
Multi-agency scalable architecture
Budget and Logistics
Budget:
$25,000 to $40,000 fixed price
Timeline: 12 weeks
Start Date:
Immediate
Timezone:
Must overlap at least 4 hours with AEST
Mandatory Proposal Requirements
Your proposal must include:
LinkedIn profile showing ML Engineering background
GitHub profile with at least 3 RAG codebases
A 2 minute Loom video walking through a production RAG system you built
Answers to technical questions listed below
Two client references from previous RAG projects
Automatic Rejection Criteria
- Data Analyst or BI focused profiles
- No production RAG examples
- Agencies or teams. Individual engineers only
- Use of Supabase or pgvector
- Batch ETL architectures instead of real-time systems
- Generic proposals without proof of work
- Missing LinkedIn, GitHub, or Loom links
Mandatory skills:
Python, Machine Learning, Natural Language Processing, Artificial Intelligence, Deep Learning, PyTorch, Docker
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