HireMO

Location: Anywhere in Country

At EY, we're all in to shape your future with confidence.

We'll help you succeed in a globally connected powerhouse of diverse teams and take your career wherever you want it to go. Join EY and help to build a better working world.

The opportunity

AI products and agents are only as useful as the knowledge they can reach, interpret, and trust. You will shape the shared foundation that connects enterprise information and expert know-how, giving teams current, permission-aware context with evidence that users can inspect.

This is a high-impact role at the intersection of information architecture, semantic systems, retrieval, data engineering, and AI product design. You will turn fragmented information into dependable organizational memory, prove difficult choices in working systems, and create reusable patterns that help teams build differentiated experiences without recreating context, provenance, and access controls for every use case.

About Challenger

Challenger is a new EY program focused on building the firm's next generation of services. It pursues opportunities where AI enables a fundamentally different service and way of delivering it, including rethinking existing offerings and developing new ones.

Small, dedicated teams build and test ideas in real conditions. Solutions that demonstrate clear business value and operational readiness transition to the business for adoption and scale.

Challenger Core develops and maintains the shared technology, methods, and risk and quality guardrails that support this work. Each team contributes reusable tools and practices, helping make the next service faster and less costly to build.

Your key responsibilities

+ Set the knowledge direction. Own the target architecture, roadmap, and decision principles for the knowledge, retrieval, and grounding capabilities used by AI products and agents.

+ Design organizational memory. Create a federated approach that respects domain ownership while establishing shared meaning, durable interfaces, and clear lifecycle expectations.

+ Translate business questions. Work with domain experts to define the concepts, relationships, and boundaries that AI systems need to interpret information accurately.

+ Engineer trusted ingestion. Shape reusable patterns that bring varied sources into the platform while preserving authority, lineage, freshness, licensing, and uncertainty.

+ Build effective retrieval. Combine search, semantic, graph, and structured methods based on the problem, making relevance, latency, cost, and explainability trade-offs visible.

+ Make evidence inspectable. Ensure context includes citations, provenance, quality signals, and evaluation hooks so users and teams can understand why an answer should be trusted.

+ Embed policy in the path. Partner with security, privacy, records, and legal teams to make permitted use, access, retention, and deletion requirements technically enforceable.

+ Prove the architecture. Lead lighthouse implementations from prototype through production, using working systems to resolve choices and establish a credible path to scale.

+ Operate and improve. Define service objectives, observability, incident learning, and quality measures that keep knowledge capabilities current and dependable after launch.

+ Build shared capability. Create reference components and onboarding practices, mentor engineers and stewards, and guide cross-functional architecture decisions.

Skills and attributes for success

+ Information architecture: Deep understanding of domain modeling, semantics, taxonomies, ontologies, knowledge graphs, and the judgment to choose the simplest durable approach.

+ Retrieval expertise: Strong command of search relevance, hybrid retrieval, ranking, grounded generation, context design, and evaluation.

+ Data engineering depth: Ability to design reliable pipelines and contracts across enterprise data, documents, events, and operational systems.

+ Trust by design: Practical knowledge of provenance, quality, access, privacy, regional, retention, and lifecycle requirements in production systems.

+ Product mindset: Ability to connect architecture choices to user outcomes, adoption, operability, and the economics of knowledge workloads.

+ Influential leadership: Clear communication and facilitation skills, with the ability to align domain owners, engineers, risk teams, and senior leaders around complex trade-offs.

To qualify you must have

+ Bachelor's Degree in a relevant field of study.

+ 9-12+ years of relevant experience.

+ Relevant experience: Substantial experience in data, information, solution, or platform architecture, including ownership of production AI, search, or knowledge systems.

+ Enterprise delivery: A record of delivering a shared knowledge, search, graph, or grounded-AI capability across varied sources and organizational boundaries.

+ Hands-on technical depth: Practical fluency with semantic, graph, or retrieval technologies, strong SQL, and a general-purpose programming language.

+ Data integration: Experience designing pipelines that connect analytical, document, event, and operational information with clear production controls.

+ Policy-aware architecture: Evidence of implementing access, provenance, quality, and lifecycle controls through the production retrieval path.

+ Architecture judgment: The ability to use prototypes and operational evidence to make sound trade-offs rather than defaulting to a particular vendor or pattern.

+ Cross-functional leadership: Experience guiding complex decisions and raising engineering capability through influence, collaboration, and mentoring.

+ Travel and On-site requirements: This role requires up to 50% travel and flexibility to go into the office in a hybrid capacity.

Ideally, you'll also have

+ Regulated environments: Delivery experience where residency, retention, confidentiality, or assurance requirements materially shape the architecture.

+ Complex knowledge domains: Experience with multilingual or multimodal knowledge, large graphs or indexes, federated query, or fast-changing information.

+ Production scale: Experience balancing relevance, freshness, latency, resilience, and cost for high-volume knowledge workloads.

+ Evaluation expertise: Experience designing meaningful measures for retrieval quality, groundedness, citation accuracy, and authorization behavior.

+ Industry contribution: Practical contributions to semantic, data, interoperability, AI operations, open-source, research, or standards communities.

We welcome exceptional candidates from product, service design, operations, transformation, policy, domain, and entrepreneurial backgrounds; no particular degree or certification is required.

What we offer you

At EY, we harness our collective strength to empower you to shape your future with confidence through professional growth, personal fulfillment and an inclusive culture. Learn more at ey.com/us/careers.

+ We offer a comprehensive compensation and benef...

Apply Externally

Jobs

AI Native Data and Knowledge Architect - Senior Manager

Summary

You Need a MO Jobs Account First

Jobs

Jobs

AI Native Data and Knowledge Architect - Senior Manager

Summary

Do You Have a MO Jobs Account?

First, Let's Find Your MO Jobs Account

Create an Account to Apply

You Need a MO Jobs Account First

HireMO Terms of Use

1. Acceptance and Changes

2. A No-Cost Public Workforce Service

3. Eligibility, Youth, and Authority

4. Accounts and Security

5. Accuracy Attestation and Program Information

6. Job Seeker Terms

7. Employer Terms

8. Assessments and Matching

9. Artificial Intelligence Features

10. Public Records and Required Notices

11. User Content and Permissions

12. Acceptable Use

13. Intellectual Property and Government Marks

14. Other Users and Third-Party Services

15. Electronic Communications

16. Suspension, Termination, and Records

17. Disclaimers

18. Limitation of Liability and Sovereign Immunity

19. Employer Indemnification

20. Governing Law and General Terms

21. Contact, Accessibility, and Complaints