CT Engineering - Senior Domain Architect AI - EY GDS

Senior Domain Architect – EY Fabric AI, Post-Sales Position Summary You 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 Job

Define EY Fabric AI reference architectures covering AI Factory, OpenAI integrations, GenAI workloads, model orchestration, agentic patterns, retrieval-based approaches, deployment, evaluation, monitoring, and lifecycle management Create 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 controls Develop AI accelerators including architecture blueprints, starter kits, integration templates, deployment patterns, evaluation frameworks, monitoring patterns, reusable prompts, configuration guidance, and handoff packs Ensure AI accelerators are easy to consume by Forward Deployed Engineers, delivery teams, and segment-aligned teams Define guardrails for responsible AI delivery, including access controls, data boundaries, auditability, monitoring, human oversight, and operational risk management Partner with AI Product, Engineering, Security, Risk, and Reliability teams to align post-sales patterns with platform roadmap and governance expectations Act as the senior escalation point for complex AI architecture questions that go beyond standard implementation playbooks Convert recurring AI implementation challenges into reusable patterns, playbooks, and accelerators Define clear handoff expectations between Presales, Domain Architecture, Forward Deployed Engineering, and ongoing support teams Govern exceptions to standard AI patterns and determine whether new supported patterns are required Maintain AI architecture assets in agreed EY Fabric knowledge repositories such as GitHub, SharePoint, Teams, and internal knowledge hubs Measure AI architecture effectiveness through pattern adoption, reduction in bespoke builds, implementation speed, operational quality, and reuse of accelerators Mentor mid-level AI Architects and help build post-sales AI architecture maturity across EY Fabric

Analytical and Decision-Making Responsibilities

Evaluate AI solution designs for scalability, security, privacy, cost, reliability, observability, and operational readiness Decide when an AI use case should follow an existing standard pattern versus requiring a new architecture pattern Prioritize AI accelerator development based on recurring demand, risk profile, delivery complexity, and adoption impact Identify common sources of AI delivery friction and drive platform-level or pattern-level improvements Assess whether AI implementations are aligned to EY Fabric guardrails and responsible AI expectations

Knowledge and Skills Required

Strong experience designing and governing production AI, ML, or GenAI systems Familiarity with AI Factory concepts, OpenAI-style integrations, model orchestration, RAG patterns, evaluation, monitoring, and lifecycle management Strong cloud architecture fundamentals including identity, networking, security, logging, and observability Understanding of responsible AI, data governance, security, risk, and operational controls