# Flywire — Senior Director, Enterprise AI & Architecture ## Posting metadata - Original JD posting: https://builtin.com/job/senior-director-enterprise-ai-architecture/10099107 - Archived JD capture: https://files.keithsteward.com/Flywire/Keith_Steward_Senior_Director_Enterprise_AI_Architecture_4438241949_JD.md - Provider: Built In public job posting; originally discovered through LinkedIn search/result context. - Location/source location: Boston, MA - Work model: Hybrid, Boston, Massachusetts; exact office cadence is not disclosed - Compensation: $200,000-$250,000 base - Travel: Not disclosed by the source posting - Posted date: 2026-08-03 in Built In structured data - Exact posting time: Unavailable / not disclosed by the source posting - Repost signal at re-probe: Reposted 14 Days Ago on Built In - LinkedIn relative posting time at original capture: 2 weeks ago - Applicants: Unavailable / not disclosed on the re-probed source posting - Resume/cover artifact generation time: 2026-08-16 06:15:02 PM EDT for the cover PDF and 2026-08-16 06:15:03 PM EDT for the resume PDF - Corrected Markdown capture generation time: 2026-08-17 07:01:37 PM EDT - Elapsed since posting: Exact elapsed time unavailable because the source does not disclose a posting time of day ## Fit decision - Positioning track: Executive leader - Central mandate: Found and lead Flywire's centralized Enterprise AI organization, platform architecture, governance, engineering enablement, adoption, and measurable value delivery. - Fit outcome: PASS - Weighted fit score: 83.3% - Keyword diagnostic summary: 20/20 significant JD terms covered in the resume (100.0%); missing significant terms: none - Validation: PASS: content, positioning, word count, PDF page count, text extraction, publication, and public URL checks completed. ## Keyword diagnostic - Significant JD terms checked: Enterprise AI, strategy, architecture, platform, LLM, RAG, agents, MCP, vector databases, governance, Responsible AI, security, privacy, observability, evaluation, adoption, automation, APIs, cloud, executive. - Covered in resume: Enterprise AI, strategy, architecture, platform, LLM, RAG, agents, MCP, vector databases, governance, Responsible AI, security, privacy, observability, evaluation, adoption, automation, APIs, cloud, executive. - Missing significant terms: none. - Diagnostic interpretation: Keyword coverage is strong, but the application rests on evidence-led fit rather than term stuffing. The resume uses terms where Keith has supporting source evidence from AssistX, AWS, MassMutual, SuccessKPI, NorthBay, and earlier architecture/engineering leadership. - Unsupported or weakly supported JD terms were not overstated: PCI-DSS, SOX, and formal FinOps ownership remain gap/caveat items rather than asserted experience. ## Hard and material gaps - Hard gaps: None found that block the application. The role is Boston hybrid and compensation meets Keith's disclosed threshold. - Material gap: The posting is hybrid in Boston, but exact office cadence is not disclosed. This should be verified before investing too much interview time. - Material gap: Travel is not disclosed by the source posting. - Material gap: Applicant count is unavailable/not disclosed on the re-probed Built In source. - Material gap: Keith has strong regulated-enterprise governance, privacy, auditability, and cost-control evidence, but direct PCI-DSS, SOX, and formal FinOps ownership are not established in the source materials. - Material gap: Keith has enterprise-scale advisory and transformation evidence, but his current founder organization is smaller than Flywire's 1,200+ employee environment. ## Evidence map | Requirement | Weight | Evidence score | Keith evidence | |---|---:|---:|---| | Found and lead a centralized Enterprise AI organization | 3 | 3/3 | Keith founded and operates Intelligent DataWorks, recruited and technically led an 11-engineer AI development team, built AI/engineering/data organizations in senior roles, and founded AWS global technical field communities for Big Data and AI/ML. | | Own Enterprise AI strategy, roadmap, operating model, standards, and measurable outcomes | 3 | 3/3 | At IDW he owns AI product strategy, platform architecture, roadmap, work breakdown, delivery priorities, and measurable workflow outcomes; at TriMark and MassMutual he owned enterprise data/privacy/AI governance roadmaps and delivery. | | Architect reusable GenAI platform capabilities: LLM access, RAG, vector search, agents, MCP, APIs, observability, and controls | 3 | 3/3 | AssistX is a production GenAI platform with LLMs, AI agents, RAG, business data, vector search, deterministic orchestration, FastAPI, PostgreSQL/PGVector, AWS infrastructure, evaluation, observability, human oversight, and cost controls. | | Scale AI adoption through enablement, playbooks, productivity tools, communities of practice, and AI-assisted engineering standards | 2 | 3/3 | Keith trained 180+ AWS architects, delivered 50+ AI/data/cloud presentations, created reusable adoption assets, grew the AWS AI/ML technical community to 80+ specialists, and trained IDW engineers on architecture, SDLC, tooling, and Agile delivery. | | Embed Responsible AI, governance, security, privacy, sensitive-data handling, auditability, human oversight, and production readiness | 2 | 3/3 | At MassMutual Keith delivered cloud-native privacy-rights systems, advised AI Governance & Ethics on MLOps, model transparency, bias, drift, auditability, and Responsible AI architecture, and applied governance/guardrail patterns in AssistX. | | Lead multidisciplinary teams across architecture, AI engineering, platform, governance, security, contractors, vendors, and cross-functional partners | 2 | 3/3 | Keith led an 11-engineer IDW team, a 24-person NorthBay accelerator team, a 25-person CambridgeSoft cloud-development team, and AWS cross-functional/global technical communities while partnering with executives, product, security, data, and business stakeholders. | | Deliver enterprise-scale transformation and shared platform capability in regulated or highly governed environments | 3 | 2/3 | Keith advised 200+ AWS enterprise customers including financial-services and healthcare organizations, delivered MassMutual privacy/AI-governance initiatives, and built shared platforms; gap is that his current company scale is smaller than Flywire's 1,200+ employee environment. | | Partner with executives and report on milestones, ROI, risk posture, strategy, and board/executive-level communication | 2 | 3/3 | Keith has C-suite/SVP advisory evidence, AWS enterprise advisory experience, senior-director/founder operating roles, roadmap ownership, delivery accountability, and quantified outcomes such as 70+ automations, 20+ workflows, 50x acceleration, and eight-month production release. | | Support compliance and cost governance including PCI-DSS, SOX, FinOps, model cost monitoring, and cloud cost controls | 2 | 1/3 | Keith has regulated privacy, AI governance, auditability, cloud cost-control, and LLM cost-control evidence, but PCI-DSS, SOX, and formal FinOps ownership are not established in the source materials. | | Meet location/work-model constraints for Boston hybrid role | 3 | 2/3 | Keith is Boston-based and the role is Boston hybrid, but the source does not disclose exact office cadence, travel requirements, or applicant count. | Weighted fit calculation: 50 weighted evidence points out of 60 possible weighted points = 83.3%. ## Artifact metadata - Public resume URL: https://bit.ly/4xK01ez - Public resume final URL: https://files.keithsteward.com/Flywire/Keith_Steward_Senior_Director_Enterprise_AI_Architecture_4438241949_Resume.pdf - Public cover letter URL: https://bit.ly/4id1Axa - Public cover letter final URL: https://files.keithsteward.com/Flywire/Keith_Steward_Senior_Director_Enterprise_AI_Architecture_4438241949_Cover_Letter.pdf - Resume PDF: 2 pages, 268405 bytes - Cover PDF: 1 page, 20954 bytes - Resume word count: 976 - Cover letter word count: 204 - LinkedIn note: Hi XXXXX - I'm interested in Flywire's Enterprise AI leadership role. Built a production AI platform with 70+ automations across 20+ workflows and led governance and adoption in regulated enterprises. I'd value connecting - LinkedIn note character count: 221 characters by deterministic Python `len()` after ASCII punctuation normalization. ## Validation summary - Source re-probe: PASS. Original JD posting returned HTTP 200, `text/html; charset=utf-8`, final URL `https://builtin.com/job/senior-director-enterprise-ai-architecture/10099107`. - Archive re-probe before correction: PASS. Archived JD capture returned HTTP 200, `text/markdown`, final URL `https://files.keithsteward.com/Flywire/Keith_Steward_Senior_Director_Enterprise_AI_Architecture_4438241949_JD.md`. - Resume public URL re-probe: PASS. Bitly returned HTTP 200, `application/pdf`, final URL `https://files.keithsteward.com/Flywire/Keith_Steward_Senior_Director_Enterprise_AI_Architecture_4438241949_Resume.pdf`, 268405 bytes. - Cover public URL re-probe: PASS. Bitly returned HTTP 200, `application/pdf`, final URL `https://files.keithsteward.com/Flywire/Keith_Steward_Senior_Director_Enterprise_AI_Architecture_4438241949_Cover_Letter.pdf`, 20954 bytes. - Source/archive distinction: PASS. Original JD posting and Archived JD capture are distinct URLs and resolve to distinct MIME types. - Final published archive URL with underscores: `https://files.keithsteward.com/Flywire/Keith_Steward_Senior_Director_Enterprise_AI_Architecture_4438241949_JD.md`. - PDF page counts and sizes: resume 2 pages / 268405 bytes; cover 1 page / 20954 bytes. - Markdown-only correction: PASS. No resume or cover PDFs were regenerated. - Normalized JD preservation: PASS. The full normalized JD body below is preserved from the original capture. ## Full normalized job description Are you ready to trade your job for a journey? Become a FlyMate! Passion, excitement & global collaboration are all core to what it means to be a FlyMate. At Flywire, we’re on a mission to deliver the world’s most important and complex payments. We use our Flywire Advantage - the combination of our next-gen payments platform, proprietary payment network and vertical specific software, to help our clients get paid, and help their customers pay with ease - no matter where they are in the world. What more do we need to truly be unstoppable? Perhaps, that is you! Who we are: Flywire is a global payments enablement and software company, founded more than a decade ago to solve high-stakes, high-value payments in higher education. We’ve since scaled into new regions and industry verticals and expanded our product offerings to deliver meaningful value to our clients around the world. Today we support more than 5,100 clients across the global education, healthcare, travel & B2B industries, with diverse payment methods across 240 countries & territories and more than 140 currencies. With over 1,200 global FlyMates, representing more than 40 nationalities, and in 12 offices world-wide, we’re looking for FlyMates to join the next stage of our journey as we continue to grow. Job Description The Opportunity 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. Key Responsibilities AI Platforms, Architecture & Engineering Enablement 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. Business Capability Enablement & Adoption 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. Responsible AI, Governance & Risk Partnership 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 FinOps guardrails, cost allocation models, and real-time AI spend dashboards. Embed responsible AI principles — PCI-DSS, SOX compliance, ethics, and explainability — into every initiative. Team Leadership, Delivery & Enterprise Collaboration 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. Qualifications Here's What We're Looking For 15+ years of progressive technology leadership experience, including senior responsibility for engineering, architecture, platforms, data, infrastructure, automation, AI, digital transformation, or enterprise technology delivery, including 5+ years managing multi-disciplinary engineering or architecture teams. Experience at large Enterprise, enabling enterprise 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 Organization. Strong understanding of Generative AI concepts and implementation patterns, including LLMs, RAG pipelines, agentic AI frameworks, enterprise ML deployment patterns, SLMs, embeddings, prompt engineering, retrieval-augmented generation, 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 connecting AI systems to enterprise tools, data sources, APIs, and workflow actions in a secure and governed manner. 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 related field required. Leadership Attributes Inspirational thought leader with passion for building and scaling AI-enabled technology and business capabilities. Pragmatic, hands-on leader with strong bias for action and measurable outcomes. Strategic yet technical, with ability to dive deep into architecture, engineering, security, data, operations, and business process details. 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. Experienced at defining and influencing organizational strategy, inclusive of board and executive level communications(written and verbal). 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 (PCI-DSS, SOX).. Experience with FinOps practices and cloud cost governance for AI/ML workloads. Preferred Qualifications 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 related field. Additional Information Submit today and get started! We are excited to get to know you! Throughout our process you can expect to meet different FlyMates including the Hiring Manager and other Flymates. Your Talent Acquisition Partner will walk you through the steps and be your “go-to” person for questions. Flywire is an equal opportunity employer and follows a policy of administering all employment decisions and personnel actions without regard to race, color, religion, sex, pregnancy, gender identity, national origin, age, ancestry, physical or mental disability, sexual orientation, genetic disposition or carrier status, veteran status, or any other category protected under applicable national, federal, state or local law. The US base salary range for this full-time position is $200,000 - $250,000 and benefits. Our salary ranges are determined by role, position level, and location. The range displayed on this job posting reflects the minimum and maximum target for new hire salaries for the position across all US locations. Within the range, individual pay is determined by work location and several other factors, including job-related skills, experience, relevant education and training.