Join EY as an AI Architect, focusing on Enterprise GenAI, Agentic AI, and AI Platform Architecture. This role offers a unique opportunity to build a career at the intersection of global scale, inclusive culture, and cutting-edge technology. Contribute your voice and perspective to enhance EY's impact and foster a better working world.

As an AI Architect, you will be instrumental in designing and delivering secure, scalable, and production-ready enterprise AI solutions. This position blends hands-on architecture with consulting leadership across Generative AI, Agentic AI, retrieval-augmented generation (RAG), AI/ML, cloud platforms, and modern application engineering. Your expertise will translate business priorities into target-state architectures, reusable patterns, and implementation roadmaps, guiding multidisciplinary teams from initial discovery through deployment and operationalization.

We are seeking candidates who have a proven track record of delivering enterprise or client-facing AI solutions in pre-production or production environments. The ideal candidate can articulate use cases, architectures, personal contributions, controls, delivery approaches, and outcomes. Demonstrations, certifications, and personal projects alone are not sufficient; practical experience is key.

Lead discovery and architecture workshops to clarify business outcomes and non-functional requirements, transforming them into scalable AI solution designs and delivery roadmaps. Architect advanced 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 comprehensive reference architectures and reusable patterns for document ingestion, chunking, embeddings, vector and hybrid search, grounding, prompt workflows, model routing, and enterprise integrations. Select appropriate models, cloud services, vector stores, orchestration frameworks, and evaluation approaches based on critical requirements such as security, quality, latency, cost, scalability, and maintainability. Design API-first, event-driven, and microservices-based integrations with enterprise applications, data platforms, workflow systems, and user experience layers. Establish robust AI evaluation and observability metrics covering retrieval quality, groundedness, accuracy, hallucination risk, agent trajectories, tool execution, latency, cost, and user experience. Embed Responsible AI principles, 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 leveraging containers, Kubernetes, managed services, CI/CD, infrastructure as code, model/LLM operations, monitoring, and release controls. Lead architecture reviews, technical design reviews, code reviews, and production-readiness assessments, adeptly troubleshooting complex issues and guiding performance optimization efforts. Collaborate effectively with business stakeholders, product owners, data scientists, engineers, UX teams, security, and platform teams to ensure alignment throughout the design and adoption phases. Contribute to proposals, RFP responses, estimates, executive presentations, accelerators, reusable assets, and the overall development of the AI practice. Lead and mentor architects and engineers, promote adherence to engineering standards, and foster capability development through coaching and knowledge sharing initiatives.

Demonstrate over 10 years of professional experience in AI, data, analytics, software engineering, or digital transformation, with substantial responsibility for solution architecture and end-to-end delivery. Possess strong hands-on experience in designing and deploying enterprise-scale AI/ML, GenAI, RAG, or Agentic AI solutions in client-facing environments. Exhibit proven experience leading cross-functional teams, managing architecture governance, engaging stakeholders, and overseeing complex delivery programs. Clearly articulate architecture decisions, trade-offs, and business value to both technical and executive audiences. Possess a deep understanding of LLMs, prompt engineering, RAG, Graph RAG, Agentic RAG, embeddings, vector and hybrid search, knowledge graphs, model evaluation, and fine-tuning approaches. Have hands-on experience with agent frameworks such as Microsoft Agent Framework, LangGraph, LangChain, AutoGen, CrewAI, or Google Agent SDK, and familiarity with the Model Context Protocol (MCP). Demonstrate strong experience with at least one enterprise cloud AI ecosystem: Microsoft Azure AI Foundry and Azure OpenAI; AWS Bedrock; or GCP Vertex AI and Gemini. Multi-cloud exposure is a plus. Possess experience with data and AI platforms like Databricks, Azure AI Search, Microsoft Fabric, Synapse, BigQuery, or equivalent enterprise data services. Show strong proficiency in Python and SQL, with a working knowledge of REST APIs, FastAPI, JSON, asynchronous processing, microservices, and event-driven architecture. Have experience with vector databases, enterprise search, relational and NoSQL data stores, caching, and analytics stores. Understand ML, deep learning, NLP, predictive analytics, and the end-to-end AI lifecycle. Be experienced with Docker, Kubernetes or OpenShift, Git, CI/CD, automated testing, infrastructure as code, MLOps, LLMOps, observability, and production support. Exhibit strong knowledge of enterprise architecture, data governance, model risk, Responsible AI, privacy, cybersecurity, accessibility, and regulatory controls. Possess strong client engagement, workshop facilitation, stakeholder management, presentation, and executive communication skills. Be capable of translating complex business requirements into practical, high-quality technical architectures and phased implementation plans. Demonstrate leadership in solution estimation, delivery governance, risk management, quality assurance, mentoring, and capability building. Exhibit curiosity, structured problem-solving abilities, commercial awareness, and a commitment to continuous learning.