Product Operation Manager

we are a leading creative technology company on a mission to empower creators and brands to bring their vision to life with video. Offering cutting-edge AI tools and models for image, video, and voiceover creation, alongside high-quality creative assets and powerful editing tools, we enable creators to stay on trend and achieve their creative goals. Trusted by over 30 million creators worldwide and top brands including Google, Amazon, Microsoft, and Versace, we provide a seamless, subscription-based platform with a global license, giving creators everything they need to produce professional video content efficiently. As the Product Operations Manager, you will be the operational backbone of our generative AI strategy, sitting at the intersection of Product, Engineering, Content, and Marketing. You will drive the scaling of evolving AI capabilities into stable, high-quality production environments while ensuring our models remain reliable, cost-effective, and seamlessly integrated into the User Experience. Responsibilities Model Lifecycle & Governance: Manage the "AI Models Mapping" registry (experimental, production, and deprecated). Define the approval process for introducing new models and ensure the reproducibility of all model capabilities and configurations. Production Reliability & Performance: Maintain continuous visibility into the performance, reliability, and cost-efficiency of all production models. Conduct periodic sampling of generations to validate output quality and edge-case behavior. Feedback Loops & Insights: Analyze customer insights and user data to identify patterns. Slice and dice bug and feature request data to enable prioritization and help the product team understand where to iterate. Strategic Vendor & Stakeholder Management: Collaborate with external engineering teams (Google, OpenAI, fal.ai) to ensure model performance. Partner internally with FinOps, Growth, and BizDev to align on model routing, consumption, and spend. Operational Excellence: Build reporting dashboards, automated agents, and quarterly reports to establish facts-based product success. Lead weekly triage meetings with engineering teams to ensure clarity around priorities and execution. Knowledge Management: Act as the single source of truth for all AI model documentation, maintaining snapshots of live and discontinued models and their specific configurations. GenAI native applications management - Build/Submission processes for internal and external testing, approval and release.