Senior Domain Architect – EY Fabric AI, Post-SalesPosition SummaryYou are a strategic Architect within the EY Fabric post-sales organization, accountable for defining repeatable AI and GenAI architecture patterns that enable EY Fabric adoption at scale.
Your role is to ensure AI Factory, OpenAI-related capabilities, GenAI integrations, model orchestration, evaluation, monitoring, and operational controls are delivered through reusable, governed, and easy-to-consume patterns rather than bespoke implementations.This role is focused on post-sales adoption, architectural integrity, and repeatability.
You will create the reference architectures, accelerators, guardrails, and implementation blueprints that help Forward Deployed Engineers and delivery teams implement AI solutions consistently while managing risk, cost, security, and operational readiness.Essential Functions Of The JobDefine EY Fabric AI reference architectures covering AI Factory, OpenAI integrations, GenAI workloads, model orchestration, agentic patterns, retrieval-based approaches, deployment, evaluation, monitoring, and lifecycle managementCreate repeatable AI design patterns for common post-sales scenarios such as GenAI application onboarding, secure model access, prompt orchestration, RAG-style solution patterns, evaluation workflows, telemetry, and operational controlsDevelop AI accelerators including architecture blueprints, starter kits, integration templates, deployment patterns, evaluation frameworks, monitoring patterns, reusable prompts, configuration guidance, and handoff packsEnsure AI accelerators are easy to consume by Forward Deployed Engineers, delivery teams, and segment-aligned teamsDefine guardrails for responsible AI delivery, including access controls, data boundaries, auditability, monitoring,



human oversight, and operational risk managementPartner with AI Product, Engineering, Security, Risk, and Reliability teams to align post-sales patterns with platform roadmap and governance expectationsAct as the senior escalation point for complex AI architecture questions that go beyond standard implementation playbooksConvert recurring AI implementation challenges into reusable patterns, playbooks, and acceleratorsDefine clear handoff expectations between Presales, Domain Architecture, Forward Deployed Engineering, and ongoing support teamsGovern exceptions to standard AI patterns and determine whether new supported patterns are requiredMaintain AI architecture assets in agreed EY Fabric knowledge repositories such as GitHub, SharePoint, Teams, and internal knowledge hubsMeasure AI architecture effectiveness through pattern adoption, reduction in bespoke builds, implementation speed, operational quality, and reuse of acceleratorsMentor mid-level AI Architects and help build post-sales AI architecture maturity across EY FabricAnalytical And Decision-Making ResponsibilitiesEvaluate AI solution designs for scalability, security, privacy, cost, reliability, observability, and operational readinessDecide when an AI use case should follow an existing standard pattern versus requiring a new architecture patternPrioritize AI accelerator development based on recurring demand, risk profile,



delivery complexity, and adoption impactIdentify common sources of AI delivery friction and drive platform-level or pattern-level improvementsAssess whether AI implementations are aligned to EY Fabric guardrails and responsible AI expectationsKnowledge And Skills RequiredStrong experience designing and governing production AI, ML, or GenAI systemsFamiliarity with AI Factory concepts, OpenAI-style integrations, model orchestration, RAG patterns, evaluation, monitoring, and lifecycle managementStrong cloud architecture fundamentals including identity, networking, security, logging, and observabilityUnderstanding of responsible AI, data governance, security, risk, and operational controlsExperience creating reusable AI architecture assets, templates, implementation guides, or acceleratorsAbility to communicate AI architecture decisions, tradeoffs, and risks to technical and executive stakeholdersStrong ability to work across Product, Engineering, Security, Risk, Growth, and delivery teamsSupervision ResponsibilitiesProvide technical leadership to AI Architects and senior engineersReview AI patterns, accelerators, and handoff artifacts for quality and consistencyCoach teams on using approved EY Fabric AI patterns instead of bespoke implementationsJob RequirementsBachelor's degree in Computer Science, Engineering, Data Science, Artificial Intelligence, or equivalent experience10+ years of engineering with recent GenAI architecture experience, including significant AI, ML, GenAI, or platform delivery experienceExperience building reusable AI patterns, accelerators, technical standards, or implementation playbooksAI, cloud architecture, security, or data certifications preferred
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