AI Solutions - Manager- Consulting - Location OPEN

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Technology — Data and Decision Science — AI Native Engineering Manager, AI/ML Engineer — Memory Layer & Knowledge Graph Master's preferred (CS or related technical field) • Hybrid • AI Native Engineering

Our Artificial Intelligence and Data team helps apply cutting-edge technology and techniques to bring solutions to our clients. No two days will be the same, and with constant research and development, you'll find yourself building knowledge that can be applied across a wide range of projects now, and in the future. EY is investing significantly in our agentic AI platform, and the memory layer is the engineering foundation of that platform. We are hiring a Manager, AI/ML Engineer to build the memory layer end-to-end: the graph databases, ontologies, vector stores, retrieval services, and grounding pipelines that power our cognitive harness and the agents that run on top of it. You will work closely with our data-science leadership on memory representation and retrieval quality, and you will own the engineering delivery and production-readiness of the memory layer itself.

As a Manager in AI Native Engineering, you will play a pivotal role in delivering the memory infrastructure that underpins EY's agentic AI offerings. You will work with a wide variety of clients to deliver the latest data science and big data technologies. Your teams will design and build scalable solutions that unify, enrich, and derive insights from varied data sources across a broad technology landscape. You will help our clients navigate the complex world of modern data science, analytics, and software engineering. We'll look to you to provide technical guidance and perform technical development tasks to ensure data science solutions are properly engineered and maintained to support the ongoing business needs of our clients — and to build the memory layer that makes those solutions possible.

Architect and build the memory layer of EY's cognitive harness end-to-end graph databases, vector stores, hybrid retrieval services, ontology services, memory APIs, and the integration into agent runtimes. Design and implement knowledge graphs and ontologies that support agent grounding vocabulary, schema rules, instances, provenance, and cross-domain mappings (sector and functional ontologies for Finance, Risk, Tax, Supply Chain, HR). Build and operate graph database infrastructure (Neo4j, Spanner Graph, Neptune, TigerGraph, or similar) schema design, ingestion, query optimization, and integration with the broader data platform. Engineer the vector and hybrid retrieval stack embeddings pipelines, vector indices (Vertex AI Vector Search, OpenSearch, Pinecone, Weaviate, pgvector), reranking, and lexical-plus-dense retrieval services. Implement grounding pipelines that connect agent runtimes to the memory layer with low latency, citation tracking, and hallucination guardrails. Lead a team of AI/ML engineers and data engineers set technical standards, run code and design reviews, and mentor on production engineering rigor. Partner with data-science leadership on memory representation, retrieval quality, and evaluation, translating science prototypes into hardened production services. Stay abreast of AI and data trends new graph paradigms, embedding models, retrieval techniques, agent frameworks (Google ADK, Bedrock AgentCore, LangGraph) and recommend tools and patterns that fit our clients' existing ecosystems. Apply combined business and technical knowledge to develop and execute target memory architectures that enable implementation, monitoring, and ongoing evolution of agentic AI at scale.

You will consistently deliver quality client services, focusing on more complex, judgmental, and specialized issues surrounding agentic AI, memory infrastructure, and emerging foundation-model technology. You will demonstrate deep technical capabilities and lead through building.

Strong AI/ML engineer who builds comfortable owning a memory service from schema to API to deploy, and writing the code that gets it there. Deep, hands-on knowledge of graph databases (Neo4j, Spanner Graph, Neptune, TigerGraph, or Stardog) — schema design, query languages (Cypher, GQL, SPARQL, Gremlin), and operating graph infrastructure in production. Strong grasp of ontologies and knowledge representation vocabulary, schema rules, instances, axioms, provenance and how ontologies support grounding and reasoning for AI agents. Solid working knowledge of cognitive harness / agent-runtime architectures memory, tools, policies, evaluation, observability and how memory infrastructure plugs into them. Hands-on experience with vector databases and hybrid retrieval — embeddings, ANN indexes, reranking, query rewriting, and semantic caching. Strong software engineering fundamentals Python (and ideally one of Java / Go / TypeScript), API design, testing, CI/CD, containerization, and observability. Experience designing and operating production AI systems on at least one major cloud (GCP, AWS, Azure, Databricks) including IAM, network controls, encryption, and responsible-AI guardrails. Excellent communication skills able to explain memory and graph concepts to clients, and able to defend engineering decisions to architects, data scientists, and partners.

Master's preferred in Computer Science, Software Engineering, Data Engineering, or a closely related technical field. Bachelor's with strong applied experience also considered. 6+ years of applied AI/ML or data engineering experience, with at least 2 years leading engineering teams or major workstreams. Demonstrable production experience with graph databases (Neo4j, Spanner Graph, Neptune, TigerGraph, Stardog, or similar), including schema design and query tuning. Production experience building retrieval / RAG / memory systems vector indices, hybrid retrieval, embedding pipelines, reranking, and grounding. Strong software engineering proficiency in Python and the modern AI/ML stack (PyTorch / TensorFlow, Hugging Face, LangChain / LangGraph, vector databases). Practical experience with at least one agent framework (Google ADK, Bedrock AgentCore, LangGraph, AutoGen, OpenAI Agents SDK) and one major cloud AI platform (Vertex AI, Bedrock, Azure AI Foundry, Databricks Mosaic AI). Experience with semantic web standards (RDF, OWL, SHACL, SPARQL) and modern property-graph approaches in the same codebase. Open-source contributions to graph, retrieval, ontology, or agent-runtime projects. Prior consulting, product, or hyperscaler experience — comfortable in a fast, ambiguous environment with senior stakeholders.

We are looking for an engineering leader who genuinely loves building infrastructure someone who sees graphs, ontologies, and memory services not as research artifacts but as production systems that have to run, scale, audit, and evolve. You will thrive here if you want to lead a team, ship to Fortune 500 clients across multiple industries, and treat the memory layer as the most important engineered asset in the agentic AI stack.

We offer a comprehensive compensation and benefits package where you'll be rewarded based on your performance and recognized for the value you bring to the business. In addition, our Total Rewards package includes medical and dental coverage, pension and 401(k) plans, and a wide range of paid time off options. Transformative leadership insights, coaching, and confidence to be the leader the world needs. Diverse and inclusive culture embraced for who you are, empowered to use your voice to help others find theirs.

We’ll empower you in a flexible environment, and fuel you and your extraordinary talents in a diverse and inclusive culture of globally connected teams. We offer a comprehensive compensation and benefits package where you’ll be rewarded based on your performance and recognized for the value you bring to the business. In addition, our Total Rewards package includes medical and dental coverage, pension and 401(k) plans, and a wide range of paid time off options. Our expectation is for most people in external, client serving roles to work together in person 40-60% of the time over the course of an engagement, project or year. Under our flexible vacation policy, you’ll decide how much vacation time you need based on your own personal circumstances. You’ll also be granted time off for designated EY Paid Holidays, Winter/Summer breaks, Personal/Family Care, and other leaves of absence when needed to support your physical, financial, and emotional well-being.

EY accepts applications for this position on an on-going basis.

EY focuses on high-ethical standards and integrity among its employees and expects all candidates to demonstrate these qualities.

EY | Building a better working world

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