Senior Director of Enterprise AI & Architecture

Responsibilities

• Flywire is building a centralized Enterprise AI organization to govern, scale, and accelerate AI adoption across the business
• The Sr. Director, Enterprise AI & Architecture will found and lead this function, establishing the enterprise-wide standards, governance model, shared platform strategy, and talent infrastructure needed to deliver measurable business value
• This is a high-visibility role at the intersection of strategy, technology, and compliance in the highly regulated sectors
• Define and own the Enterprise AI strategy, roadmap, and operating model in alignment with Build and lead team spanning architecture, AI engineering, platform, governance, and security. Leading the strategy and delivery of foundational AI platform capabilities that support secure, scalable, and reusable AI-enabled applications
• Serve as strategic leader for the AI Center of Excellence; represent the Enterprise AI org to the Executive team reporting on milestones, ROI, and risk posture
• Define architecture patterns for AI-First applications, copilots, intelligent workflows, automation agents, enterprise knowledge solutions, and reusable AI components. Oversee a risk-tiered governance and architecture review process; own the technology exception process
• Guide platform capabilities such as model access, retrieval frameworks, vector databases, enterprise knowledge integration, prompt and response controls, observability, and governance guardrails
• Partnering to define standards for AI-assisted software engineering practices across the SDLC, including coding, testing, documentation, requirements analysis, code review, and engineering workflow automation
• Partner with Applications, Engineering, Infrastructure, Operations, Architecture, Security, and Data teams to pilot, refine, and scale AI-enabled practices over time
• Establish and maintain enterprise AI/ML standards, frameworks, playbooks, and reference architectures. Driving adoption of a centralized AI platform including LLM gateway, model registry, agent frameworks, and shared APIs
• Evaluate emerging AI vendors and technologies; run pilot programs and proofs-of-concept
• Prevent shadow AI proliferation by providing self-service resources and pre-approved patterns that make governance easy.company OKRs
• Partner with engineering, operations, finance, customer service, and other business functions to identify and deliver high-value AI-enabled process improvements
• Lead the development of AI capabilities such as decision support, workflow automation, document intelligence, knowledge assistance, summarization, triage, productivity tools, and service quality improvements
• Help business teams move from AI ideas to practical use cases with clear outcomes, adoption plans, controls, and value measures
• Lead enterprise enablement of AI productivity tools such as Gemini, ChatGPT, Claude, and related assistants, including standards, training, adoption practices, and usage guardrails
• Build reusable playbooks, enablement models, and communities of practice that raise AI fluency across IT and the broader organization
• Embed security, privacy, responsible AI, sensitive data handling, human oversight, vendor risk, and production readiness into AI platforms, business use cases, engineering practices, operations, and employee tools
• Partner with Security, Legal, Risk, Compliance, Data, Architecture, and business teams to define and operationalize enterprise AI governance
• Create governance models that support responsible experimentation while protecting customers, employees, business partners, and enterprise data
• Partner with Finance to implement Fin Ops guardrails, cost allocation models, and real-time AI spend dashboards
• Embed responsible AI principles - PCI-DSS, SOX compliance, ethics, and explainability - into every initiative
• Build and lead a small, high-performing AI-First organization with strong architecture, engineering, automation, platform, and delivery capabilities
• Lead from the front with a hands-on, roll-up-the-sleeves leadership style and strong ownership of outcomes. Owning delivery across scope, schedule, budget, quality, risk, dependencies, adoption, and business value
• Develop talent and create a culture of curiosity, accountability, disciplined experimentation, continuous learning, and measurable outcomes

• Familiarity with enterprise AI platform capabilities such as model access gateways, model catalogs, AI orchestration layers, policy enforcement, prompt and response controls, observability, cost monitoring, and usage governance
• Bachelor’s degree in Computer Science, Engineering, Information Systems, Data Science, or related field required
• Understanding of AI/ML model lifecycle practices, including model selection, experimentation, validation controls, performance monitoring, drift detection, feedback loops, auditability, and responsible production deployment
• Experience with Agentic AI patterns, including autonomous or semi-autonomous agents, tool/function calling, workflow orchestration,…