Conversational Interfaces Lead

Join as the Conversational Interfaces Lead, where you will drive the continuous improvement and optimization of our conversational AI product portfolio. You will own the performance, quality, and evolution of partner-facing conversational interfaces, delivering measurable improvements in partner self-service adoption, answer quality, and support efficiency. You will collaborate closely with various teams and represent the partner perspective in decision-making.

• Conduire l'amélioration continue et l'optimisation des interfaces conversationnelles, en se basant sur les retours des partenaires et l'analyse des données de conversation.
• Posséder la performance, la qualité et l'évolution des interfaces conversationnelles, en mettant en œuvre des améliorations mesurables.
• Collaborer étroitement avec d'autres responsables dans différents domaines du portefeuille GenAI pour assurer une collaboration efficace et éviter les doublons.

• Business Acumen - Frames day-to-day work in terms of answer quality, self-service rates, and contact reduction; connects fixes and improvements to DPE commercial metrics and CS outcomes
• AI Operations & Continuous Improvement - Proven track record of running the day-to-day operations of a live AI/conversational product, driving continuous quality and content improvements based on real usage and partner feedback
• Proven experience (4+ years) in Product Operations, Product Management, or Technical Program Management, with proven focus on driving continuous improvement and performance optimization of Conversational AI products in production
• Content & Prompt Execution - Hands-on experience writing and iterating on prompts, dialogue flows, and AI-served content at scale; understands how content and prompt changes affect AI output and can execute improvements directly, not just define standards for others
• Project Management — Methodical in breaking down complex cross-functional work and delivering against timelines independently
• Data-Driven Evaluation — Comfortable designing and interpreting AI quality evaluations, including human-in-the-loop frameworks, session analysis, and in-product measurement — not reliant solely on automated judge scores
• Content & Conversational Governance — Understands how content quality and guidelines affect AI output; experience working with content teams to define and maintain standards for AI-served or AI-generated content at scale
• Stakeholder Communication — Bridges technical and non-technical audiences; experienced in positioning AI work internally and translating complex technical concepts into clear business value propositions
• Technical Literacy — Understands knowledge architecture, retrieval systems, skills frameworks, evaluation methodologies, and GenAI infrastructure (RAG, LLM evaluation, model updates) well enough to challenge and prioritize technical decisions and scope boundaries without owning the underlying infrastructure implementation