AI Product Owner - Operations

Belong - AI Product Owner - Operations AI Product Owner - Operations Argentina Product & Design – Product / Full time / On-site apply for this job 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 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. 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. 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. 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. 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 We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. 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