Job Description
Principal AI Systems Product Manager
(AI Agents, Cross-System Intelligence, CRM + Operations Automation)
Read This First (Hard Filter)
This is not a prompt writer, no-code automations, or “AI assistant” role.
If your experience is limited to:
Writing prompts
Using ChatGPT as a tool
Zapier-only workflows
Shipping isolated AI features without system ownership
Do not apply.
This role is for someone who designs, owns, and delivers AI systems as products, translates executive vision into coherent AI capabilities, and leads engineers to build them correctly.
The Mission
We are building an internal AI operating system for a real estate private credit and private equity firm.
Your responsibility is to own the product vision, system design, and execution of this AI platform — ensuring it becomes a reliable, extensible layer that operates across the firm’s core business tools.
This system must:
Read across Asana, Cloze.com, Gmail, Airtable, Dropbox
Reason across time, commitments, relationships, and deals
Surface risk: missed follow-ups, stalled deals, broken promises
Write back into systems safely, with guardrails
Run on triggers and schedules
Evolve into a cohesive, cross-system AI product ecosystem
Think Jarvis for a real operating business, not a chatbot.
Your Role (What You Will Actually Do)
You are not the primary coder.
You are the AI systems product owner responsible for turning business intent into production AI capabilities and guiding a team of fractional / contract AI engineers to deliver them.
1. Translate Executive Vision into AI Products
Work directly with the CEO to understand:
Business priorities
Risk points
Decision-making bottlenecks
Define what AI should do, not just how it’s built
Break vision into concrete AI-enabled products and capabilities
2. Own the AI System Architecture (at a Product Level)
Define the overall system design, including:
Agent roles and responsibilities
Cross-system context and data flow
Read vs write boundaries
Human-in-the-loop approval points
Ensure the system is cohesive, not a collection of disconnected automations
3. Lead and Coordinate AI Engineers
Oversee a team of fractional / contract AI developers and engineers
Provide clear requirements, acceptance criteria, and architectural direction
Review designs and implementations for:
Correctness
Safety
Maintainability
Ensure engineers build toward the product vision, not ad hoc solutions
4. Design AI Capabilities as Products
You will oversee the delivery of:
An agentic AI layer
A primary orchestration agent
Optional specialist agents (CRM, tasks, email, data)
Cross-system intelligence
Normalized context from structured + unstructured data
Reasoning across tools and time
Action execution
Task creation and updates
CRM notes and relationship updates
Data record updates
Drafted communications for approval
Triggers and automation
Time-based (daily, weekly)
Event-based (emails, overdue tasks, stalled deals)
5. Governance, Risk, and Control
Define guardrails for AI actions
Ensure:
Scoped permissions
Read vs write separation
Explicit approvals for sensitive or destructive actions
Plan for failure modes and recovery
Required Background (Non-Negotiable)
You must have hands-on experience owning AI systems, even if you were not the primary coder.
You should be able to confidently reason about:
LLM agent architectures and tool calling
Claude and/or OpenAI capabilities and tradeoffs
MCP or MCP-style multi-tool architectures
API-based integrations (CRMs, task tools, email, databases)
OAuth, permissions, and access control
State, memory, and long-running agent behavior
Systems that run unattended in production
You must be able to explain how an AI system:
Safely reads from one system
Decides what matters
Writes into another system
Avoids causing operational damage
Deliverables You Will Own
System Architecture
Clear diagrams or written explanations
Separation of concerns
Product roadmap for AI capabilities
Phase 1
Read-only intelligence layer
“What you missed” and “what’s at risk” reporting
Phase 2
Write-back actions with guardrails
Human-in-the-loop approvals
Documentation
How the system works
How to extend it
How to maintain and govern it
How to Apply (Strict)
Your proposal must include:
A specific example of an AI system you owned that interacted with multiple tools
Your high-level product and system architecture for this project
Which LLM you would start with and why (from a product perspective)
How you think about memory, permissions, and failure states
Your availability
Anything vague, generic, or purely technical without product ownership will be declined.
Engagement Model
Initial scoped project
Long-term engagement likely for the right person
We value judgment, product thinking, and system quality over speed
Mandatory Inclusion
Describe a system you owned where an AI agent read from one application and wrote actions into another.
What went wrong, and how did you correct or govern it?
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