Cmms (maintenance Management System) Product Jobs

1 job found

1mo ago

Product Manager – Maintenance Platform

TMS LLC Arlington, VA

Arlington, VA

1mo ago Closed

Contract Cmms (maintenance Management System) Customer Experience Partner Management

This position is no longer accepting applications (closed Jul 9, 2026).

Job Description

TMS LLC is seeking a Product Manager to lead the development and scaling of the Maintenance Platform across communities, guiding strategy, execution, and cross-functional alignment to improve maintenance operations, resident experience, and operational efficiency.

Responsibilities

Define and execute the product roadmap for the maintenance platform, including work order management, inspections, preventive maintenance, and resident communication.
Collaborate with Operations, Engineering, and external vendors to translate business needs into scalable product solutions.
Own end-to-end delivery from requirements and prioritization through launch and iteration.
Streamline workflows across scheduling, technician productivity, and automated resident engagement.
Lead the integration strategy to ensure seamless data flow and interoperability with systems such as PMS, CRM, and middleware.
Leverage data and user feedback to continuously improve product performance and adoption.
Support pilot programs and scale successful solutions across the portfolio.

Requirements

5+ years of product management experience, preferably in B2B SaaS, operations, or platform products.
Experience building workflow-based products such as work order systems, CRM, or operations platforms.
Familiarity with AI/ML-driven products or automation tools; hands-on experience is not required, but strong product sense is expected.
Strong stakeholder management and cross-functional leadership skills.
Analytical mindset with experience using data to inform product decisions.

Technologies

PMS
CRM
Middleware

AI / DATA EXPECTATIONS

Identify opportunities to leverage AI to automate workflows, including work order intake, routing, and communication.
Partner with engineering on AI-enabled features such as recommendation engines, automation, and workflow optimization.
Define success metrics and evaluate AI performance (accuracy, efficiency, user adoption).
Ensure responsible AI usage, including data quality, transparency, and operational reliability.

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