AI Architect

At EY, we’re all in to shape your future with confidence. 

We’ll help you succeed in a globally connected powerhouse of diverse teams and take your career wherever you want it to go. 

Join EY and help to build a better working world. 

 

Senior Manager - AI Architect - AI Tech Transformation - TC 

The opportunity

We are seeking a Senior Manager, AI Architect specializing in generative AI to lead the architecture and delivery of complex GenAI engagements for our clients. This is a senior delivery role: you will own the architecture of significant GenAI workstreams and programs, lead delivery teams day to day, and ensure solutions scale reliably in production. You will also translate complex technology into clear, persuasive narratives for senior client stakeholders, making strong communication central to the role.

Your key responsibilities

List of key deliverables/responsibilities Delivery of Complex Engagements

• Own the architecture and technical delivery of complex GenAI engagements or major workstreams, from design through production rollout.
• Lead cross-functional delivery teams (architects, engineers, data specialists) across the engagement lifecycle.
• Ensure solutions are enterprise-grade: scalable, secure, resilient, performant, and cost-optimized in production.
• Drive delivery governance: architecture decisions, technical risk management, dependencies, quality gates, and blocker resolution.
• Apply and extend engineering standards, reusable frameworks, and accelerators to improve delivery velocity and consistency.
• Own technical outcomes and accountability for delivery quality, timelines, and measurable business impact within your scope.

Technical Direction

• Define end-to-end generative AI architectures: LLM selection, RAG pipelines, prompt orchestration, agentic workflows, and enterprise integration.
• Architect solutions for high-value insurance use cases (claims intake automation, submission triage, document intelligence, underwriting copilots, policy Q&A, contact-center copilots).
• Design RAG systems at scale: chunking strategies, embeddings, vector stores, retrieval tuning, and grounding to reduce hallucination.
• Design multi-agent and tool-calling workflows that automate complex insurance processes.
• Set LLMOps and quality standards: prompt engineering, evaluation, guardrails, monitoring, versioning, and cost/latency optimization.
• Apply responsible AI governance: hallucination controls, PII handling, bias mitigation, explainability, auditability, and regulatory compliance (NAIC, GDPR, EU AI Act).