Hiring nowKnowledge graphs and semantic systems

AI Knowledge Architect

Also called Enterprise Knowledge Architect

An AI Knowledge Architect designs how an application organises, governs and updates what it knows. That structure decides whether retrieval is useful and trustworthy, and whether anyone can be held to account for changes.

What they do

The work starts with the questions the application must answer and the sources it draws on. The architect models entities, relationships and constraints, often in a graph or ontology, and decides how they map to existing databases. They define who owns each body of knowledge, how changes are proposed and approved, and how old facts are retired. They trace where each fact came from and check data quality. They sit with engineers, domain experts and retrieval teams to test whether the structure returns useful results, then revise it when it does not.

What a strong hire delivers

In the first months, a strong hire produces a knowledge architecture that engineers can build against and domain experts recognise as accurate. They agree a lifecycle policy covering ownership, review and retirement of knowledge. They put basic provenance and quality checks in place. They also show, with real queries, where retrieval improved and where gaps remain, so the team knows what to fix next.

How you can tell they are good

Ask them to walk through a knowledge model they designed, and why they drew boundaries where they did. Good candidates explain trade-offs, show how they tested retrieval against real questions, and describe what happened when the model was wrong. Ask who approved changes. If they only talk about tools or diagrams, and not stewardship, be careful.

Why it is hard to fill

Few people carry both halves of this work. Some are strong ontologists with little sense of retrieval in live products. Others are engineers who treat structure as an afterthought. Titles vary widely, and many candidates have never been called an architect. The governance side is often invisible on a CV. We look for evidence of both design and upkeep.

Where to find them

They tend to come from knowledge engineering, information science, library and taxonomy work, data modelling and database teams. Some have built ontologies in healthcare, finance, publishing or government. Others come from search and enterprise data groups, or from graph database vendors and consultancies. Look for people who have kept a knowledge model alive after launch, not only built one.

Indicative pay

Not enough published evidence yet to give a fair range for this role, which is common for work this new. We benchmark it for you as part of a search.

Interview questions for an AI Knowledge Architect

  1. Tell us about a knowledge model you designed. What did it represent, and what would you change now?

    A strong answer names real entities, relationships and constraints, and honest regrets. Listen for how it served actual queries. Be wary of answers that describe only tools, or claim the design needed no changes.

  2. How do you decide between a graph, a relational schema and a simple document structure for a given problem?

    Good answers start from the questions to be answered, query patterns and change rates. They admit that a graph is sometimes unnecessary. Worry about a fixed preference for one technology regardless of need.

4 more questions, with what a strong answer covers, are in the Role Kit.

Job description you can post

We are hiring an AI Knowledge Architect to design how our products organise and govern knowledge. You will shape the structures behind our retrieval, and the rules for keeping them accurate. We want someone who builds models people can use and maintain.

What you will do

  • Model entities, relationships and constraints for our applications
  • Map knowledge structures to existing databases and sources
  • Define ownership, review and retirement rules for knowledge
  • Set up provenance and data quality checks
  • Test retrieval usefulness with real questions and improve the design
  • Work with engineers and domain experts to resolve definitions
  • Document the architecture so others can build on it

What you will bring

  • Shown work designing ontologies, taxonomies or knowledge graphs
  • Strong SQL and comfort with data modelling
  • Understanding of information retrieval and how structure affects results
  • Experience with provenance and data quality practice
  • Experience with graph databases or RDF/OWL tooling
  • Clear written explanations for technical and non-technical readers

Nice to have

  • Experience supporting retrieval for language model applications
  • Background in a regulated or specialist domain
  • Experience running a governance or stewardship process

Free to use and adapt. If it saves you time, a link back to this page is appreciated.

Skills that matter

  • Knowledge representationAdvancedRepresent facts, relations and constraints explicitly
  • Information retrievalAdvancedRetrieve and rank relevant documents or entities
  • Data quality and provenanceWorkingTrack dataset origin, validity and transformations
  • SQLWorkingQuery and validate relational data

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How we wrote this

Role definitions are our own editorial work, checked against occupational classifications, standards, research and employer pages. Titles vary by company, and this is a description of the work, not a job advert.

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