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Semantic AI Engineer

AbbVie · North Chicago, IL · Full time · Information Technology

Posted Aug 24, 2026 · We last checked this listing on Oct 6, 2026

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Likely interview questions for this role

Written from this job description, not a generic list. Each one notes what the interviewer is really checking.

About your past

How would you write documentation and reusable examples so a team can consume an ontology service without talking to you?

What they are checking: The adoption-enabling documentation this posting makes a duty.

This sits in the Reference Data Architecture team inside the Enterprise Knowledge Accelerator at North Chicago, asks for five years with a bachelor's degree, and names tools for agents alongside conventional interfaces. What do you want to know about how mature this platform is and who is consuming it today?

What they are checking: Whether they check how real the platform is before joining it.

What would you do

Tell me about a mapping between two vocabularies that could not be made clean. How did you handle the ambiguity?

What they are checking: Honest handling of the cases where semantics do not line up.

How do you set a quality gate on a semantic asset without making it so slow that teams route around you?

What they are checking: The governance balance this role shares with the standards expert.

A team wants a query expansion feature that will return more results but some of them will be wrong. How do you decide?

What they are checking: Tradeoff judgment between recall and precision in a search context.

About the work

This role turns ontologies and controlled vocabularies into services other platforms can call. Walk me through how you would design one of those services so teams actually use it.

What they are checking: The productization of semantic assets, which is what separates this role from ontology work.

Explain entity resolution to someone building a product data platform. Where does it usually get hard?

What they are checking: Depth on one of the named capabilities, explained plainly.

How do you combine a knowledge graph with a generative model so the model's output is grounded rather than plausible?

What they are checking: The combination of semantic assets and generative artificial intelligence this team is built on.

What do the findable, accessible, interoperable and reusable data principles change about how you build a service, in practice?

What they are checking: Whether the FAIR principles this posting cites are operational for them or a slogan.

Walk me through your experience with the web ontology language, the resource description framework and SPARQL. What have you actually built with them?

What they are checking: Concrete work in the standards this posting names.

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The full job description

As published by AbbVie.

AbbVie’s global Information Research (IR) group has a mission to unlock information that makes cures possible. Within IR, the Enterprise Knowledge Accelerator (EKA) team serves as a trusted knowledge partner for clients in R&D and the Corporate Business & Strategy Office (CBSO), helping drive innovation, knowledge sharing, and faster decisions that support advancement of AbbVie’s pipeline. EKA is a global organization with presence in Lake County, Illinois, Boston, Massachusetts, and Ludwigshafen, Germany. Within EKA, the Reference Data Architecture (RDA) team is building Ontoverse to develop AbbVie’s enterprise semantic capabilities by combining GenAI technologies, reference data expertise, and semantic architecture. RDA creates the semantic layer that connects high-quality internal and external ontologies, controlled vocabularies, and reference standards with major company data initiatives and platforms, making knowledge more connected, computable, and actionable across the enterprise. The Semantic AI Engineer will build and operationalize software capabilities that expose these semantic assets through Enterprise Ontology Services (EOS) for use in platforms such as Enterprise Product Master Data (EPMD), Dataverse, and controlled technology development frameworks like SHIFT. The role applies software engineering and semantic knowledge representation to create reusable components, APIs, MCP tools, agents, and AI-enabled services that support discovery, inference, harmonization, data integration, and decision-making at scale, while contributing to CI/CD practices that enable reliable delivery and ongoing operational support. Working closely with platform, product, ontology, data, and governance teams, the role will also help deliver implementation solutions that embed FAIR data principles into GenAI-generated software assets from the start. Responsibilities: • Design, build, and operationalize AI-driven semantic solutions that embed enterprise ontologies and semantic assets into Enterprise Product Master Data initiative, the Dataverse, and related enterprise platforms through EOS. • Engineer and translate ontologies, controlled vocabularies, mappings, semantic contracts, and reference standards into machine-consumable services, APIs, agent tools, and reusable software components. • Enable semantic knowledge structures to actively drive inference, search, normalization, query expansion, entity resolution, and data integration across enterprise-scale workflows. • Partner with the Standards & Governance Expert to define implementation patterns, decision processes, and quality gates that embed FAIR data principles into SHIFT-generated software assets from project initiation. • Collaborate with platform, product, data, ontology, and governance teams to align semantic services with business requirements, enterprise architecture, and scalable delivery practices. • Develop and maintain technical documentation, integration patterns, reusable examples, and guidance that help teams consume EOS capabilities consistently and effectively. • Understand and adhere to applicable corporate standards, including code of conduct, data security, GxP compliance where relevant, software development lifecycle expectations, and responsible AI practices. • Monitor and be attuned to new technology trends relevant for knowledge analysis and insights discovery. • Achieve great results, while demonstrating key AbbVie values and behaviors. • Bachelor’s Degree in Data Science, Computer Science, Informatics, Software Engineering, Semantic Technologies or any related field and 5 years of experience; or Master’s Degree and 4 years of experience. • Demonstrated experience building production-quality data, software, AI, or semantic technology solutions, including APIs, MCP tools, agents, services, integrations, or reusable platform components, common technologies, and formats (e.g., Rest APIs, XML, JSON, Python libraries such as Pydantic). • Working knowledge of ontologies, controlled vocabularies, knowledge graphs, semantic standards (OWL, RDF, SPARQL), metadata, mapping, normalization, and FAIR data principles. • Ability to translate business and governance requirements into scalable technical designs that make semantic assets accessible to applications, AI agents, search, and data integration workflows. • Demonstrated ability to learn and apply emerging technologies, operate within timelines, and produce clear technical documentation for enterprise adoption, contributing to CI/CD practices that enable reliable delivery and ongoing operational support. • Strong collaboration, stakeholder management, and communication skills, with the ability to work across platforms, governance, ontology, and product teams. • Comfortable working in a fast paced, highly collaborative, dynamic work environment with constantly shifting priorities. • Demonstrate an interest in working cross-functionally and in inter-disciplinary teams with an ability to effectively communicate, both verbally and in writing, with scientists and non-scientists. • Innate scientific curiosity, technical creativity, and innovative thinking. Motivated to break new ground in the field of information analysis and insights generation. • Fluency in English Applicable only to applicants applying to a position in any location with pay disclosure requirements under state or local law: ​ • The compensation range described below is the range of possible base pay compensation that the Company believes in good faith it will pay for this role at the time of this posting based on the job grade for this position. Individual compensation paid within this range will depend on many factors including geographic location, and we may ultimately pay more or less than the posted range. This range may be modified in the future. ​ • We offer a comprehensive package of benefits including paid time off (vacation, holidays, sick), medical/dental/vision insurance and 401(k) to eligible employees.​ • This job is eligible to participate in our short-term incentive programs. ​ Note: No amount of pay is considered to be wages or compensation until such amount is earned, vested, and determinable. The amount and availability of any bonus, commission, incentive, benefits, or any other form of compensation and benefits that are allocable to a particular employee remains in the Company's sole and absolute discretion unless and until paid and may be modified at the Company’s sole and absolute discretion, consistent with applicable law. ​ AbbVie is an equal opportunity employer and is committed to operating with integrity, driving innovation, transforming lives and serving our community.  Equal Opportunity Employer/Veterans/Disabled. US & Puerto Rico only - to learn more, visit https://www.abbvie.com/join-us/equal-employment-opportunity-employer.html US & Puerto Rico applicants seeking a reasonable accommodation, click here to learn more: https://www.abbvie.com/join-us/reasonable-accommodations.html

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