Senior Director, Enterprise AI & Architecture

Flywire • Boston, MA

9d • $186,615 - $266,593

About The Position

Flywire is establishing a centralized Enterprise AI organization to govern, scale, and accelerate AI adoption across the business. The Senior Director, Enterprise AI & Architecture will found and lead this function, defining enterprise-wide AI standards, governance, shared platform strategy, and talent infrastructure to deliver measurable business value. The role sits at the intersection of strategy, technology, and compliance in highly regulated sectors.

The position owns the Enterprise AI strategy, roadmap, and operating model; builds and leads a multidisciplinary organization; and establishes architecture patterns, platform capabilities, governance processes, and standards for AI-enabled applications and engineering practices. It represents the Enterprise AI organization to executives, reports on milestones, return on investment, and risk, and partners across technology and business teams to pilot and scale AI capabilities.

The role also enables business adoption by identifying high-value use cases, developing practical AI capabilities and productivity tools, and providing training, playbooks, and communities of practice. It embeds security, privacy, responsible AI, compliance, cost controls, and production readiness into platforms and use cases, while developing talent and fostering disciplined experimentation and measurable outcomes.

Requirements

• 15+ years of progressive technology leadership experience, including senior responsibility for engineering, architecture, platforms, data, infrastructure, automation, AI, digital transformation, or enterprise technology delivery.
• 5+ years managing multidisciplinary engineering or architecture teams.
• Experience at a large enterprise enabling adoption of AI productivity tools such as Gemini, ChatGPT, Claude, or similar platforms.
• Significant hands-on leadership experience with AI, machine learning, generative AI, automation, advanced analytics, intelligent platforms, developer productivity tools, or emerging technology capabilities at a large enterprise.
• Strong understanding of generative AI concepts and implementation patterns, including LLMs, RAG pipelines, agentic AI frameworks, enterprise ML deployment patterns, SLMs, embeddings, prompt engineering, vector databases, semantic search, evaluation frameworks, and enterprise knowledge integration.
• Experience with agentic AI patterns, including autonomous or semi-autonomous agents, tool/function calling, workflow orchestration, human-in-the-loop controls, guardrails, monitoring, and safe deployment practices.
• Familiarity with Model Context Protocol (MCP) or similar approaches for securely and governably connecting AI systems to enterprise tools, data sources, APIs, and workflow actions.
• Understanding of AI/ML model lifecycle practices, including model selection, experimentation, validation controls, performance monitoring, drift detection, feedback loops, auditability, and responsible production deployment.
• 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.
• Strong technical fluency across cloud platforms, APIs, microservices, data platforms, observability, automation, cybersecurity, identity, privacy, and modern engineering practices.
• Bachelor’s degree in Computer Science, Engineering, Information Systems, Data Science, or a related field.
• Proven experience leading enterprise-scale technology transformation, preferably in a regulated environment such as financial services or another highly governed industry.
• Track record of partnering with executive stakeholders and translating technology strategy into business outcomes.
• Experience defining and influencing organizational strategy, including board- and executive-level written and verbal communications.
• Demonstrated success building or leading an enterprise AI, platform engineering, or architecture function at scale.
• Proven ability to lead internal teams, contractors, vendors, and system integration partners in a fast-paced, high-accountability environment.
• Strong command of compliance requirements relevant to payments, including PCI-DSS and SOX.
• Experience with FinOps practices and cloud cost governance for AI/ML workloads.
• Pragmatic, hands-on leadership, strategic and technical fluency, a bias for action, and focus on measurable outcomes.

Nice To Haves

• Experience at a global payments, fintech, or healthcare technology company.
• Familiarity with federated delivery models and domain-led architecture teams.
• Background in responsible AI, AI ethics frameworks, or model explainability.
• MBA or advanced degree in Computer Science, Engineering, or a related field.

Responsibilities

• Define and own the Enterprise AI strategy, roadmap, and operating model.
• Build and lead a multidisciplinary team spanning architecture, AI engineering, platform, governance, and security.
• Lead 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 and represent the Enterprise AI organization 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 and own the technology exception process.
• Guide platform capabilities including model access, retrieval frameworks, vector databases, enterprise knowledge integration, prompt and response controls, observability, and governance guardrails.
• Partner to define standards for AI-assisted software engineering practices across the SDLC, including coding, testing, documentation, requirements analysis, code review, and workflow automation.
• Partner with Applications, Engineering, Infrastructure, Operations, Architecture, Security, and Data teams to pilot, refine, and scale AI-enabled practices.
• Establish and maintain enterprise AI/ML standards, frameworks, playbooks, and reference architectures.
• Drive adoption of a centralized AI platform, including an LLM gateway, model registry, agent frameworks, and shared APIs.
• Evaluate emerging AI vendors and technologies and run pilot programs and proofs of concept.
• Prevent shadow AI proliferation by providing self-service resources and pre-approved patterns that make governance easy.
• Partner with engineering, operations, finance, customer service, and other business functions to identify and deliver high-value AI-enabled process improvements.
• Lead 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 turn AI ideas into 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 to 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 enable responsible experimentation while protecting customers, employees, business partners, and enterprise data.
• Partner with Finance to implement FinOps guardrails, cost allocation models, and real-time AI spend dashboards.
• Embed responsible AI principles, PCI-DSS, SOX compliance, ethics, and explainability into initiatives.
• Own 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.

Benefits

• Employee Stock Purchase Plan (ESPP).
• Competitive time off.
• Wellbeing programs, including mental health, wellness, and Yoga/Pilates/HIIT classes.
• Talent and development programs, including Managers Taking Flight and OneFlywire Career Mobility.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior