Join EY and build a career that's as unique as you are. Leverage global scale, support, and technology to become your best self. Your perspective is vital in helping EY evolve. We're looking for a Senior AI Architect with extensive Java/J2EE expertise and hands-on AI/Generative AI delivery experience.

This role is pivotal in shaping AI-first enterprise solutions. You will embed intelligent capabilities into existing Java platforms and guide clients from initial exploration to secure, scalable production deployment. Operate at the forefront of enterprise architecture, product engineering, cloud-native modernization, and applied AI. Define target architectures, make critical technology decisions, lead engineering teams, and advise senior stakeholders on leveraging AI for measurable business and engineering outcomes.

Architect end-to-end AI-enabled enterprise applications, covering data ingestion, model orchestration, inference, APIs, user experience, and production operations. Design advanced solutions like Retrieval-Augmented Generation (RAG), semantic search, knowledge graphs, multimodal, and agentic AI, aligning them with business workflows. Develop multi-agent systems, tool-use patterns, and orchestration strategies, including Model Context Protocol (MCP) where applicable. Establish architecture blueprints, reusable patterns, non-functional requirements, and technology guardrails for scalable, secure, and responsible AI adoption. Evaluate models, frameworks, data stores, and cloud AI services based on quality, latency, security, portability, operability, and cost.

Architect enterprise solutions using Java/J2EE, Spring Boot, Spring MVC, Spring Security, and RESTful APIs. Integrate LLM-powered services, copilots, and AI agents into existing Java applications via well-governed APIs and event-driven patterns. Decompose monolithic applications into modular services and define modernization pathways that introduce AI seamlessly without disrupting business continuity. Design microservices, event-driven, and distributed architectures using technologies like Kafka or RabbitMQ, container platforms, and API management. Guide engineering teams on code quality, design patterns, performance, resilience, observability, security, and maintainability.

Design and deploy AI solutions on Azure, AWS, or GCP, utilizing appropriate managed AI services and cloud-native components. Establish robust CI/CD, DevSecOps, MLOps, and LLMOps practices, including prompt and model versioning, automated testing, deployment, monitoring, evaluation, and rollback procedures. Define AI quality and evaluation frameworks focusing on relevance, groundedness, safety, latency, reliability, and cost. Implement comprehensive observability across application, model, and agent layers, driving continuous improvement through telemetry and user feedback. Lead the transition from proof-of-concept to hardened, scalable production deployments.

Embed privacy, data governance, access control, content safety, model security, auditability, and Responsible AI principles into solution designs. Define controls for prompt injection, sensitive data exposure, unsafe outputs, model misuse, and third-party model risks. Collaborate with security, risk, legal, and compliance stakeholders to ensure AI solutions align with enterprise policies and regulatory obligations. Drive cost transparency and FinOps practices for model usage, infrastructure, and multi-cloud AI workloads.

Lead discovery workshops, architecture assessments, and design sessions with business and technology stakeholders. Translate business goals into AI roadmaps, solution options, implementation plans, and clear value propositions. Act as a trusted advisor to client leadership, communicating architecture choices, trade-offs, risks, and outcomes in executive-ready language. Provide technical leadership for pursuits, proposals, solutioning, and estimation, including developing architecture narratives and delivery models. Lead and mentor cross-functional teams of architects, Java engineers, AI engineers, data scientists, and platform specialists. Create reusable accelerators, reference implementations, knowledge assets, and engineering standards for the EAT competency.