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Klaviyo is seeking a Senior AI Systems & Solutions Architect to drive AI adoption and innovation within the Customer Success organization. This role bridges applied AI, solutions architecture, and internal enablement, focusing on translating business challenges into practical, scalable AI solutions.
You will own the full lifecycle of AI initiatives: discovering high-impact opportunities with Success leaders, rapidly prototyping solutions using Claude, ChatGPT, and other LLM platforms, and scaling successful prototypes into production-grade systems in collaboration with Klaviyo's centralized AI engineering team.
Key responsibilities include:
**Program Strategy & Discovery**: Partner with Success leadership to identify areas where AI and automation can drive efficiency and impact. Prioritize opportunities and translate ambiguous business needs into clear roadmaps with defined outcomes and milestones.
**Technical Prototyping & Productization**: Build AI-powered prototypes and workflows using LLMs, APIs, MCPs, and automation platforms. Design end-to-end intelligent workflows that integrate with existing systems. Develop effective prompts, skills, agents, and retrieval systems. Evaluate solutions using user feedback and business outcomes to drive adoption.
**Enablement & AI Literacy**: Create documentation, implementation guides, and best practices. Lead workshops and office hours to grow AI fluency across teams. Stay current on emerging AI capabilities and tools.
You are a systems thinker with strong technical foundations and passion for solving business problems with AI. You have hands-on experience building AI prototypes, understand APIs and system integrations, and communicate complex technical concepts clearly. You're comfortable partnering with engineering teams to scale solutions and thrive in a collaborative, fast-moving environment.
Preferred qualifications include 4+ years in AI solutions, solutions architecture, internal tooling, or automation; hands-on experience with Claude or other leading LLM platforms including prompt engineering and AI workflows; experience integrating AI with APIs and internal systems; and demonstrated ability to rapidly prototype and iterate based on feedback.
