Lead AI Systems Architect – Autonomous Real Estate Development Engine
Budget: $250 – $750 USD
We are seeking a world-class AI Systems Architect or Full-Stack AI Development Team to design and engineer a semi-autonomous, multi-agent ecosystem. This system is designed to disrupt traditional real estate development by automating the "Heavy Lift" of land sourcing, market intelligence, and institutional-grade investment modeling for 300+ acre master-planned communities.
Our curent flagship project, serves as a blueprint. We are now scaling this logic into a "Development-as-a-Service" (DaaS) engine that can move a project from a raw idea to a funding-ready Investment Memorandum with minimal manual friction.
Core Objective:
Build an end-to-end AI pipeline that integrates geospatial data, economic catalysts, and financial modeling to produce "Investor-Ready" documentation. The system must transition seamlessly through:
Market Sourcing → Catalyst Identification → Financial Stress-Testing → Institutional Offering.
Scope of Work & Technical Requirements:
1. Multi-Agent Orchestration (The "Brain")
* Design a modular, 15+ agent architecture (using frameworks like LangGraph, CrewAI, or AutoGen).
* Implement JSON-schema handoffs between agents to ensure 100% data integrity between layers (e.g., ensuring the Market Agent's density stats perfectly match the Pro-Forma Agent's revenue rows).
2. "Catalyst Intelligence" Module (Critical Requirement)
* Build a specialized agent capable of scraping and synthesizing high-impact economic drivers: corporate relocations, infrastructure expansions (highways/airports), and municipal zoning shifts.
* Logic: Convert job growth data into housing demand projections.
* Output: A "Why This Market / Why Now" investment narrative backed by verifiable economic data.
3. Land Sourcing & Scorecarding:
* Integrate APIs (e.g., BatchData, Land.id, or GIS layers) to identify off-market tracts.
* Develop a weighted Ranking Engine that scores parcels based on topography, utility access, and "Path of Progress" metrics.
4. Institutional Financial Modeling:
* Automate the generation of a complex Pro-Forma (IRR, NPV, Waterfall equity structures).
* Enable "Stress-Test" simulations: How does a 150-basis-point interest rate hike or a 10% construction cost overrun impact the GP/LP split?
5. Automated Artifact Generation:
* The system must output near-final drafts of:
* Private Placement Memorandums (PPM)
* Comprehensive Feasibility Studies
* Visual Site Concept Briefs
The "Safe-Guard" Architecture:
We require a "Human-in-the-Loop" (HITL) Gate. The system must pause and present a "Current Truth" summary for executive approval after the financial modeling phase before triggering high-cost document generation. Additionally, an Auditor Agent must be implemented to cross-reference legal narratives against financial tables to flag any hallucinations.
Candidate Requirements:
* Proven Experience: You have built complex, multi-agent AI systems (beyond simple RAG).
* Domain Expertise: Familiarity with Real Estate Private Equity (REPE), LTV/LTC ratios, and land development workflows.
* Technical Stack: Proficiency in Python, LLM Orchestration, API integration, and Document Engineering (Markdown/LaTeX to PDF).
* Cost Efficiency: Deep understanding of Model Tiering and Prompt Caching to maintain low per-project operational costs.
Deliverables:
* System Architecture: A fully mapped-out agent hierarchy and data-flow diagram.
* Functional Pipeline: An autonomous engine capable of producing a full investment package from a single Zip Code/Acreage input.
* Data Strategy: A robust plan for sourcing verifiable real-world economic and land data.
* Operational Documentation: A "Standard Operating Procedure" for running and scaling the system.
How to Apply:
Please submit a brief proposal outlining your experience with Autonomous Agents and Financial Data.
Challenge Question:
How would you ensure that a 40-page Investment Memorandum drafted by an AI remains 100% consistent with a dynamic Excel-based Pro-Forma model?
Our curent flagship project, serves as a blueprint. We are now scaling this logic into a "Development-as-a-Service" (DaaS) engine that can move a project from a raw idea to a funding-ready Investment Memorandum with minimal manual friction.
Core Objective:
Build an end-to-end AI pipeline that integrates geospatial data, economic catalysts, and financial modeling to produce "Investor-Ready" documentation. The system must transition seamlessly through:
Market Sourcing → Catalyst Identification → Financial Stress-Testing → Institutional Offering.
Scope of Work & Technical Requirements:
1. Multi-Agent Orchestration (The "Brain")
* Design a modular, 15+ agent architecture (using frameworks like LangGraph, CrewAI, or AutoGen).
* Implement JSON-schema handoffs between agents to ensure 100% data integrity between layers (e.g., ensuring the Market Agent's density stats perfectly match the Pro-Forma Agent's revenue rows).
2. "Catalyst Intelligence" Module (Critical Requirement)
* Build a specialized agent capable of scraping and synthesizing high-impact economic drivers: corporate relocations, infrastructure expansions (highways/airports), and municipal zoning shifts.
* Logic: Convert job growth data into housing demand projections.
* Output: A "Why This Market / Why Now" investment narrative backed by verifiable economic data.
3. Land Sourcing & Scorecarding:
* Integrate APIs (e.g., BatchData, Land.id, or GIS layers) to identify off-market tracts.
* Develop a weighted Ranking Engine that scores parcels based on topography, utility access, and "Path of Progress" metrics.
4. Institutional Financial Modeling:
* Automate the generation of a complex Pro-Forma (IRR, NPV, Waterfall equity structures).
* Enable "Stress-Test" simulations: How does a 150-basis-point interest rate hike or a 10% construction cost overrun impact the GP/LP split?
5. Automated Artifact Generation:
* The system must output near-final drafts of:
* Private Placement Memorandums (PPM)
* Comprehensive Feasibility Studies
* Visual Site Concept Briefs
The "Safe-Guard" Architecture:
We require a "Human-in-the-Loop" (HITL) Gate. The system must pause and present a "Current Truth" summary for executive approval after the financial modeling phase before triggering high-cost document generation. Additionally, an Auditor Agent must be implemented to cross-reference legal narratives against financial tables to flag any hallucinations.
Candidate Requirements:
* Proven Experience: You have built complex, multi-agent AI systems (beyond simple RAG).
* Domain Expertise: Familiarity with Real Estate Private Equity (REPE), LTV/LTC ratios, and land development workflows.
* Technical Stack: Proficiency in Python, LLM Orchestration, API integration, and Document Engineering (Markdown/LaTeX to PDF).
* Cost Efficiency: Deep understanding of Model Tiering and Prompt Caching to maintain low per-project operational costs.
Deliverables:
* System Architecture: A fully mapped-out agent hierarchy and data-flow diagram.
* Functional Pipeline: An autonomous engine capable of producing a full investment package from a single Zip Code/Acreage input.
* Data Strategy: A robust plan for sourcing verifiable real-world economic and land data.
* Operational Documentation: A "Standard Operating Procedure" for running and scaling the system.
How to Apply:
Please submit a brief proposal outlining your experience with Autonomous Agents and Financial Data.
Challenge Question:
How would you ensure that a 40-page Investment Memorandum drafted by an AI remains 100% consistent with a dynamic Excel-based Pro-Forma model?