Principal Product Manager

Join MaintainX as a Principal Product Manager, where you will own LLMX, the company's internal AI platform. This role is critical to the success of every AI-powered feature and workflow. You will define the platform's roadmap, evaluation infrastructure, and make key decisions on models, providers, and infrastructure. The ideal candidate will have 10+ years of product management experience, a strong background in AI systems, and a commercial instinct. Benefits include flexible time off, competitive salary and equity opportunities, and comprehensive healthcare coverage.

• Posséder la feuille de route de LLMX de bout en bout, et conduire l'adoption et la qualité en fonction de métriques telles que le temps jusqu'à la première intégration, la couverture d'évaluation, la latence et le coût.
• Définir les primitives de la plateforme, y compris l'acheminement des agents, l'orchestration des compétences, les flux de travail humains dans la boucle, la mémoire des agents, la compaction des conversations, et l'interface conversationnelle de l'application.
• Travailler de manière proactive avec l'ensemble de l'organisation produit de MaintainX pour identifier les besoins futurs des fonctionnalités agentiques, avant que ces besoins ne deviennent des obstacles.

• Nice to have: experience in industrial operations or asset management
• Strong commercial instinct, connecting platform investment to enterprise differentiation, competitive positioning, and deal cycles
• A real system for staying current in LLMs and agentic AI, and the discipline to apply it without chasing every new release
• Data-driven by default: you define success in metrics, instrument your platform, and use evals to make quality visible at scale
• You've shipped production AI services that other teams built on, and you know what it takes to earn and keep their trust
• 10+ years in product management, with meaningful experience in AI platform, ML infrastructure, or developer platform roles
• Real, informed opinions on model selection, RAG vs. fine-tuning trade-offs, agentic architectures, evaluation frameworks, and latency/cost optimization
• Comfortable serving two customers at once: the internal teams building on your platform, and the enterprise buyers whose outcomes depend on it