Klaviyo-posted 3 days ago
Full-time • Senior
San Francisco, CA
501-1,000 employees

As a Senior Lead Internal AI Architect on the IT Leadership Team, you will define and execute the strategy for embedding artificial intelligence into Klaviyo's internal operations, infrastructure, and workforce experience. You will serve as the principal architect and strategist for designing secure, scalable, and efficient AI-driven systems that transform how IT delivers value to the business. Your scope will include everything from integrating AI into infrastructure and automation frameworks to building internal AI platforms for experimentation and enablement. You'll collaborate closely with IT, Security, Risk and Trust, and Engineering to modernize the company's technology foundation and enable an “AI-native” workforce. You will also act as a key influencer across the organization, shaping standards for AI adoption, driving responsible innovation, and mentoring teams as Klaviyo evolves its internal architecture toward intelligent, automated, and resilient systems.

  • Modernize Klaviyo's IT foundation by identifying opportunities to embed AI across systems from observability and automation to identity and access frameworks and by implementing AIOps practices that increase reliability and performance.
  • Build the foundation for an AI-native workforce by partnering with internal teams to safely deploy AI tools such as copilots, workflow automation, and intelligent chat interfaces that enhance employee productivity.
  • Design and govern internal AI platforms, including model access layers, sandbox environments, and experimentation frameworks for secure, compliant use of generative AI.
  • Establish architectural standards for AI integration across enterprise systems (CRM, ERP, HRIS, collaboration tools) while ensuring compliance, cost optimization, and alignment with business objectives.
  • Define the AI adoption roadmap for IT and internal operations, creating frameworks for AI maturity, capability development, and operational readiness.
  • Coach and evangelize AI literacy across technical and non-technical teams, helping stakeholders identify “AI-ready” opportunities and fostering a culture of experimentation, safety, and scalability.
  • Partner across departments — including Security, Engineering, and Data — to ensure AI-enabled systems meet organizational and regulatory requirements.
  • Establish feedback loops and continuous improvement processes to measure AI impact and refine strategies for sustainability, performance, and business alignment.
  • AI and Systems Architecture Leadership 10+ years leading cross-functional technology transformations or systems architecture teams with a focus on AI, automation, or infrastructure modernization.
  • Demonstrated success embedding AI into enterprise IT or SaaS environments, improving system reliability, scalability, and observability through AI and automation frameworks (AIOps, MLOps).
  • Strategic AI Enablement and Governance Proven ability to define and implement standards for AI access, usage, and observability, including model governance, data lineage, and compliance monitoring.
  • Experience designing internal AI platforms, APIs, and integration frameworks that connect AI systems to core business platforms.
  • Skilled in creating policies and controls for safe, ethical, and cost-optimized AI usage across distributed teams.
  • Technical Expertise Strong foundation in IT infrastructure, cloud architecture (AWS, GCP, or Azure), and data systems.
  • Working knowledge of AI/ML concepts, generative AI frameworks, and API integration design.
  • Familiarity with enterprise platforms such as Okta, Freshservice, Salesforce, or Workday, and how AI can augment these systems.
  • Experience with automation and orchestration tools (DevOps, AIOps, or workflow automation platforms).
  • Leadership and Change Management Track record of driving organizational change through technology transformation, including establishing frameworks for intake, prioritization, and execution of AI initiatives.
  • Experience mentoring engineers and technical leads, fostering growth and accountability through feedback and continuous learning.
  • Excellent communication skills with the ability to bridge technical depth and executive alignment.
  • Advanced experience with AI platform operations, MLOps pipelines, or internal AI development tooling.
  • Background in systems design or DevOps engineering with exposure to AI or automation integration at scale.
  • Familiarity with enterprise security and compliance frameworks related to AI model governance and data protection.
  • Prior experience leading internal “AI Center of Excellence” or equivalent transformation programs.
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