Product Owner, Operations (AI-First)

The Role
Belong is building the Residential Operating System: a fully integrated, AI-powered platform that
manages homes, coordinates thousands of real-world service moments, and creates authentic
belonging experiences for homeowners and residents. The member journey is the product. But
the Residential OS only delivers on that promise if the operational machinery running beneath it
is intelligent, instrumented, and self-improving.
Most companies say they are AI-first. At Belong, it means something specific: by the end of
2025, the majority of communications across sales, leasing, homecare, and concierge functions
are AI-generated. Human Advisors and Concierges handle trust-critical moments. AI agents
handle everything else: triage, scheduling, status updates, escalation routing, vendor
coordination, documentation. The operations product surface is where that architecture lives or
dies.
As Product Owner, Operations, your job is to design, deploy, and relentlessly improve the
AI-powered system that runs the homeowner and resident journey from inspection through
occupancy. You are not writing requirements for a future that engineers will build someday. You
are shipping agent-driven workflows today, measuring their quality and deflection rates next
week, and iterating the week after. This role is for someone who understands that the frontier of
operations is not better dashboards. It is autonomous systems that perform with the judgment of
your best operator, at infinite scale, at the moment the member needs it.

What You'll Own
AI agent architecture across the operational journey.
Every operational phase, from home preparation, move-in orchestration, homecare and
maintenance, to Pro coordination and vendor scheduling, has a human workflow today and an
AI-assisted target state. You will define that target state phase by phase: what the agent
handles autonomously, what triggers human review, what escalates immediately. You will write
the logic, instrument the outcomes, and own the quality bar. An agent that deflects volume but
degrades CSAT is not a win. You hold both numbers simultaneously.
The agent-human handoff model.
The Member Journey Brief is explicit: humans are deployed at trust-critical moments. AI handles
orchestration, speed, and precision behind the scenes. You are the person who defines exactly
where that line sits, and who moves it systematically as agent quality improves. You will build
confidence thresholds, fallback protocols, and human-in-the-loop checkpoints that protect the
member experience while continuously expanding the autonomous surface area.

LLM-powered communication workflows.
Belong's target is 80% AI-generated communications across operational functions by Q3. You
will own the product layer that makes this real for operations: the prompt architecture, context
retrieval pipelines, output quality review systems, and the feedback loops that improve
generation quality over time. You will define what context an agent needs to respond like a
trained Concierge, and build the retrieval and injection infrastructure that delivers it.
Foundation as the AI control panel.
Foundation is where Belong's operational teams live. Every tool your squad ships into
Foundation is either creating leverage for humans or replacing manual work with agent-driven
automation. You will define the roadmap for Foundation's evolution from task management
system to AI control panel: where agents surface for review, where exceptions queue for human
action, where quality scores and deflection rates are visible in real time.
Operational instrumentation and model feedback.
AI systems degrade without structured feedback. You will build the instrumentation that captures
ground truth: CSAT signals, escalation rates, rework rates, SLA breach patterns, and member
sentiment. You will design the feedback loops that push this signal back into model evaluation
and prompt improvement. You are not shipping a model. You are shipping a system that learns.

The AI Stack You Will Work With
• LLM-based communication generation with context injection from CRM and operational
state
• Agentic scheduling and coordination workflows (Homecare triage, Pro dispatch, vendor
coordination)
• Automated escalation routing based on signal classification
• Quality scoring and anomaly detection on agent outputs
• Retrieval-augmented generation for Concierge and Homecare agent context
You do not need to build the infrastructure from scratch. You need to define what it should do,
instrument it, and iterate it.

What Success Looks Like
90 days: Every operational phase has a documented AI target state with defined autonomous
scope, human escalation thresholds, and instrumentation in place.
6 months: AI-assisted workflows have measurably reduced manual communication volume
across at least 2 operational functions with no CSAT degradation.

Year 1: The majority of routine operational communications in your product surface are
AI-generated. Human operators are handling exceptions, escalations, and trust-critical
moments, nothing else. Failed move-in rate is below 3%. Time-to-list is trending down quarter
over quarter.

Example KPIs You Will Be Held To
• AI deflection rate vs. manual handling baseline, by operational function
• CSAT from homeowners and residents at each operational phase (the constraint:
deflection gains cannot come at CSAT cost)
• SLA compliance rates for homecare and Pro services
• Time-to-list (inspection to live listing)
• Move-in readiness rate and failed move-in rate
• Human escalation rate as a quality signal on agent confidence calibration

Who You Are
**AI systems thinker.** You do not think about AI features. You think about AI systems: input
context, output quality, fallback behavior, quality measurement, and continuous improvement
loops. You have designed or operated LLM-powered workflows in a production environment and
understand how they fail, not just how they work.
**Operationally grounded.** You have worked in environments where things break in the real
world, with real vendors, real homes, real members, and you understand that an agent
operating without the right context is more dangerous than no agent at all. You design for failure
modes first.
**Outcome obsessed.** You hold deflection rate and CSAT simultaneously. You do not celebrate
automation that degrades experience. You ship, measure, and decide based on what the
numbers actually say.
**Technically fluent.** You can write a SQL query, read a vector similarity result, reason about
retrieval quality, and understand the tradeoffs in a prompt engineering decision. You do not need
to write the code. You need to understand it well enough to make the right call.
**Cross-functional driver.** Operations, Homecare, Leasing, Vendor Ops, and Engineering all
touch your surface. You run the rituals, translate across languages, and hold the delivery
cadence.

What You Bring
• 3 to 5 years of product experience, with at least 1 to 2 years directly building or operating
AI-powered products in a production environment
• Hands-on experience with LLM integrations, prompt engineering, RAG pipelines, or
agentic workflow design
• Demonstrated ownership of operational tooling or service orchestration products in a
marketplace, logistics, or operations-intensive environment
• Proficiency with data: SQL, funnel analysis, and the ability to detect when a metric is
being gamed or misread
• Experience with AI evaluation frameworks and output quality measurement is a strong
advantage
• Prior work in consumer real estate, hospitality, or residential services is a plus