Join EY's Digital Supply Chain Team as an experienced Supply Chain AI Solution Architect. This hybrid role focuses on enterprise AI architecture, supply chain knowledge graphs, and product thinking to create secure, scalable solutions that deliver tangible business value. You'll contribute to EY's mission of building a better working world.

EY's Global Delivery Services (GDS) is a vital part of our global strategy, supporting growth through a worldwide network of service delivery centers. With over 80,000 professionals across ten locations, GDS offers comprehensive support in Consulting, Assurance, Tax, Strategy & Transactions, and Knowledge. Enablement Services provides cost-effective, innovative solutions for EY's global and local teams, covering areas like Markets, Finance, IT, and Procurement.

Architect enterprise-grade AI applications leveraging advanced patterns such as LLMs, RAG, Agentic AI, and Knowledge Graphs. Define complete AI solution architectures, including model selection, data retrieval strategies, orchestration, and integration patterns. Develop sophisticated prompting techniques, context engineering, and memory management. Design hybrid retrieval solutions using vector, semantic, and GraphRAG approaches. Establish AI platform standards for observability, evaluation, LLMOps, and cost management. Define AI security architectures to mitigate threats like prompt injection and data leakage, ensuring compliance with Responsible AI principles and privacy regulations.

Conduct architecture reviews and technical assessments to drive AI adoption. Mentor AI engineers and developers, establishing best practices and reusable frameworks. Continuously research emerging AI technologies to foster innovation. Drive stakeholder engagement through workshops and technical leadership. Collaborate with business stakeholders, product owners, and technical teams to translate requirements into scalable AI solutions.

A minimum of 12 years of experience in software engineering, cloud architecture, data engineering, or enterprise architecture is required. Demonstrated expertise in designing and implementing enterprise AI, Generative AI, and intelligent automation solutions. Practical experience with LLMs, prompt engineering, RAG, embeddings, and vector databases is essential. Strong knowledge of AI agent architectures, multi-agent systems, and tool calling capabilities. Proficiency in Python, API development, microservices, and distributed systems. Experience with major cloud platforms (Azure, AWS, GCP) and their AI services.

Solid understanding of databases, data platforms, event-driven architectures, and enterprise integration. Hands-on experience with secure integration frameworks and MCP-based integrations. Knowledge of AI evaluation frameworks, LLMOps, and AI cost optimization techniques. Familiarity with model fine-tuning, quantization, and inference optimization. Experience with multimodal AI, knowledge graphs, and advanced retrieval architectures. Excellent communication, stakeholder management, and problem-solving skills are crucial for delivering business outcomes.