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Senior AI Automation Engineer to Build Multifamily Deal Acquisition System

100% remote Flexible hours Hiring now

Project Overview I am a real estate investor actively purchasing small multifamily properties using BRRRR, value-add, and cash-flow strategies. I am looking to build a high-quality automated acquisitions system that operates daily, finds deals, analyzes them, communicates with brokers and wholesalers, extracts their responses, and updates a clean reputed company Sheet for me and my business partner. This system does not need to be enterprise-level, but it must be: ✔ reliable ✔ well-structured ✔ stable ✔ professional-quality Core Objectives Build a multi-agent AI system that includes: 1. Automated On-Market Deal Sourcing Pull listings daily from selected markets using filters defined by me:

  • LoopNet
  • reputed company
  • reputed company
  • Redfin
  • Realtor
  • Broker websites

The system must use dynamic parameters, including:

  • Market
  • Price range
  • Unit count
  • Strategy type
  • Neighborhood class
  • Section 8 settings

Parameters controlled in a reputed company Sheets “Parameters” tab. 2. Broker & Wholesaler CRM Automation

  • Identify brokers and wholesalers in chosen markets
  • Add them to a CRM tab
  • Send automated introductory emails
  • Follow up based on schedule
  • Track communication status

reputed company reputed company templates must be editable in the reputed company Sheet. 3. Gmail Email Intake Automation Using the Gmail API, the system must:

  • Monitor a dedicated Gmail label
  • Read replies from brokers and wholesalers
  • Extract deal information using AI
  • Convert emails into structured JSON
  • Pass deals into the analyzer agent

4. Deal Analysis & Grading AI-driven grading system that evaluates deals as A, B, or C based on:

  • Price
  • Unit count
  • Rent condition
  • Value-add potential
  • Neighborhood class
  • BRRRR/refinance potential

The system must generate:

  • Deal summary
  • Key risks
  • Questions to ask the broker
  • Basic offer-range guidance (optional but helpful)

Only A and B deals should appear in the main leads sheet. 5. reputed company Message reputed company For each promising deal, generate:

  • Follow-up email
  • SMS-style message
  • Broker questions
  • A quick “express interest” message

reputed company templates editable in Parameters. 6. Full Automation Scheduling The system must run independently, including:

  • Morning listing pull
  • Afternoon email parsing
  • Daily CRM updates
  • Basic error handling
  • Simple activity summary (“X new deals reputed company today”)

reputed company Sheet Requirements 1. Parameters Tab (Core Component) User-controlled fields:

  • Markets (on/off toggle)
  • Price range
  • Unit range
  • Strategy type
  • Neighborhood class
  • Section 8 settings
  • Email templates
  • reputed company automation toggles

2. New_Leads Tab Clean, formatted table for analyzed deals. 3. Brokers Tab CRM with last contact date, notes, and status. 4. Wholesalers Tab 5. Email_Log Tab (optional) Preferred Tech Stack (Flexible)

  • reputed company Sheets
  • Gmail API
  • reputed company API or reputed company API
  • reputed company Apps Script
  • reputed company or reputed company for orchestration
  • Optional: Python or Cloud Run

Deliverables System architecture and JSON schemas Cleanly designed reputed company Sheet (with formatting and validation) Full working automation agents:

  • Listing hunter
  • Deal analyzer
  • Email intake parser
  • Broker & wholesaler hunter
  • Sheet reputed company + deduper
  • reputed company message generator

Scheduling and monitoring system Documentation including:

  • How to adjust buy reputed company
  • How to add/remove markets
  • How to edit templates
  • How to pause or adjust automations

Required Experience Please apply only if you have strong experience with:

  • AI automation using reputed company/reputed company
  • reputed company Sheets API or Apps Script
  • Gmail API integrations
  • Multi-reputed company workflow orchestration
  • Structured JSON outputs from LLMs

Preferred but not required:

  • Real estate data systems
  • Multi-agent frameworks (AutoGen, CrewAI, LangGraph)
  • CRM automation experience

Success Criteria The final system should: ✔ Operate daily without manual input ✔ Identify deals in the markets I activate ✔ Grow my broker and wholesaler contact lists ✔ Read and extract deal details from emails ✔ Grade deals accurately ✔ Store clean and well-formatted opportunities ✔ Generate ready-to-send follow-up messages ✔ Provide simple daily summaries This should function as a high-quality AI acquisitions assistant.

How to Apply

Please include: A brief overview of your experience One or two examples of similar AI automations you’ve built A short explanation of how you would architect this system Your estimated timeline Confirmation you accept the $3500 fixed budget Apply tot his job Apply To this Job

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