EY - GDS Consulting - AIA - AI Archictect- Manager

At EY, you’ll have the chance to build a career as unique as you are, with the global scale, support, inclusive culture and technology to become the best version of you. And we’re counting on your unique voice and perspective to help EY become even better, too. Join us and build an exceptional experience for yourself, and a better working world for all.

AI ArchitectEnterprise GenAI, Agentic AI and AI Platform ArchitectureCareer Family AIA - AI / GenAI / Agentic AIRole Type Full TimeThe opportunity

We are seeking an experienced AI Architect to lead the design and delivery of secure, scalable and production-ready enterprise AI solutions. The role combines hands-on architecture with consulting leadership across Generative AI, Agentic AI, retrieval-augmented generation (RAG), AI/ML, cloud platforms and modern application engineering. The architect will translate business priorities into target-state architectures, reusable patterns and implementation roadmaps while guiding multidisciplinary teams from discovery through deployment and operationalization.

The ideal candidate has delivered enterprise or client-facing AI solutions in pre-production or production environments and can explain the use case, architecture, personal contribution, controls, delivery approach and outcomes. Certifications, personal projects and demonstrations alone are not sufficient.

Your key responsibilities

• Lead discovery and architecture workshops, clarify business outcomes and non-functional requirements, and convert them into scalable AI solution designs and delivery roadmaps.

• Architect LLM applications, copilots, RAG and Graph RAG solutions, autonomous agents, multi-agent workflows, tool/function calling, memory patterns and human-in-the-loop controls.

• Define reference architectures and reusable patterns for document ingestion, chunking, embeddings, vector and hybrid search, grounding, prompt workflows, model routing and enterprise integrations.

• Select fit-for-purpose models, cloud services, vector stores, orchestration frameworks and evaluation approaches based on security, quality, latency, cost, scalability and maintainability requirements.

• Design API-first, event-driven and microservices-based integrations with enterprise applications, data platforms, workflow systems and user experience layers.

• Establish AI evaluation and observability covering retrieval quality, groundedness, accuracy, hallucination risk, agent trajectories, tool execution, latency, cost and user experience.

• Embed Responsible AI, privacy, security and compliance controls including PII protection, access control, auditability, prompt-injection mitigation, content safety, secure tool execution and data-leakage prevention.

• Define cloud-native deployment and operations patterns using containers, Kubernetes or managed services, CI/CD, infrastructure as code, model/LLM operations, monitoring and release controls.