• Own the vision, strategy, and roadmap for FloQast's Core AI Platform, establishing it as the foundational layer for all AI initiatives across the company • Partner closely with senior Product and Engineering leadership to align the platform roadmap with company-wide AI goals and emerging customer needs • Identify opportunities to consolidate redundant AI work (e.g., Document parsing, RAG implementations) into shared platform capabilities • Serve as the AI infrastructure partner for product teams across FloQast, understanding their AI use cases and translating them into platform requirements • Conduct discovery sessions with internal teams to identify common AI patterns, pain points, and opportunities for shared solutions • Build a platform that enables non-AI engineers to leverage sophisticated AI capabilities through intuitive APIs and templates • Create the "paved road" for AI development—making it easier to do AI the right way than to build custom solutions • Design and prioritize platform components including RAG frameworks, agent orchestration systems, model evaluation pipelines, state management, and monitoring tools • Ensure the platform supports enterprise-grade requirements: audibility, explainability, reliability, and security • Define success metrics for platform adoption and AI feature quality across the organization • Bring deep technical understanding of AI systems—their capabilities, limitations, and operational challenges—to guide platform architecture decisions • Establish evaluation frameworks and quality standards for AI features built on the platform • Partner with Engineering to make sound long-term technical investments while delivering near-term value • Champion AI best practices including prompt engineering, model evaluation, human-in-the-loop workflows, and responsible AI principles • Act as the primary evangelist for the Core AI Platform, building internal awareness and driving adoption across product teams • Develop documentation, tutorials, and enablement materials that make the platform accessible to diverse technical audiences • Measure and communicate platform impact through metrics like reduction in time-to-market for AI features, consistency of AI quality, and engineering efficiency gains