AI Native Data and Knowledge Architect - Senior Manager

EY•Hartford, DC

3d•$144,000 - $374,000•Hybrid

About The Position

EY is hiring a Senior Manager to shape the shared knowledge, retrieval, and grounding foundation used by AI products and agents. The role sits at the intersection of information architecture, semantic systems, retrieval, data engineering, and AI product design. It focuses on turning fragmented information into dependable organizational memory, with current, permission-aware context and inspectable evidence. The position is part of Challenger, an EY program that develops and tests new services enabled by AI. Challenger Core builds and maintains shared technology, methods, and risk and quality guardrails, and the role contributes reusable tools and practices that help teams develop services more efficiently. The architect owns the target architecture, roadmap, and decision principles for knowledge capabilities; establishes federated organizational memory and reusable ingestion and retrieval patterns; and ensures provenance, quality, access, privacy, retention, and lifecycle controls are built into production systems. The role leads lighthouse implementations from prototype through production, sets operational and quality measures, and builds shared capability by creating reference components, onboarding practices, and mentoring engineers and stewards. Travel of up to 50% is required, along with flexibility to work in the office in a hybrid capacity.

Requirements

Bachelor’s degree in a relevant field of study.
9–12+ years of relevant experience.
Substantial experience in data, information, solution, or platform architecture, including ownership of production AI, search, or knowledge systems.
A record of delivering a shared knowledge, search, graph, or grounded-AI capability across varied sources and organizational boundaries.
Practical fluency with semantic, graph, or retrieval technologies, strong SQL skills, and a general-purpose programming language.
Experience designing pipelines that connect analytical, document, event, and operational information with clear production controls.
Evidence of implementing access, provenance, quality, and lifecycle controls through the production retrieval path.
Ability to use prototypes and operational evidence to make sound architectural trade-offs rather than defaulting to a particular vendor or pattern.
Experience guiding complex decisions and raising engineering capability through influence, collaboration, and mentoring.
Ability to travel up to 50% and flexibility to work in the office in a hybrid capacity.
Strong communication and facilitation skills to align domain owners, engineers, risk teams, and senior leaders around complex trade-offs.

Nice To Haves

Delivery experience in regulated environments where residency, retention, confidentiality, or assurance requirements materially shape architecture.
Experience with multilingual or multimodal knowledge, large graphs or indexes, federated query, or fast-changing information.
Experience balancing relevance, freshness, latency, resilience, and cost for high-volume knowledge workloads.
Experience designing measures for retrieval quality, groundedness, citation accuracy, and authorization behavior.
Practical contributions to semantic, data, interoperability, AI operations, open-source, research, or standards communities.

Responsibilities

Own the target architecture, roadmap, and decision principles for knowledge, retrieval, and grounding capabilities used by AI products and agents.
Create a federated organizational-memory approach that respects domain ownership while establishing shared meaning, durable interfaces, and clear lifecycle expectations.
Work with domain experts to define concepts, relationships, and boundaries that help AI systems interpret information accurately.
Shape reusable ingestion patterns for varied sources while preserving authority, lineage, freshness, licensing, and uncertainty.
Combine search, semantic, graph, and structured retrieval methods according to the problem, making relevance, latency, cost, and explainability trade-offs visible.
Ensure context includes citations, provenance, quality signals, and evaluation hooks so users and teams can assess answer trustworthiness.
Partner with security, privacy, records, and legal teams to enforce permitted use, access, retention, and deletion requirements technically.
Lead lighthouse implementations from prototype through production, using working systems to resolve architectural choices and establish a path to scale.
Define service objectives, observability, incident learning, and quality measures to keep knowledge capabilities current and dependable after launch.
Create reference components and onboarding practices, mentor engineers and stewards, and guide cross-functional architecture decisions.