This role pays less than
99%
of similar roles. Most pay
$158,175–$242,562
— the shaded band above.
At the midpoint, this role pays about
$110k
versus about
$200k
for comparable roles.
Based on 240 similar postings.
Employer
About AbbVie
AbbVie is a global biopharmaceutical company focused on discovering and delivering innovative medicines and solutions in immunology, oncology, neuroscience, and eye care. Its products include Humira, Skyrizi, and Rinvoq.
AbbVie currently has
297 open roles
on FindRole.
Listed pay typically runs
$109,500–$208,500
across 271 roles with salary data.
The Semantic AI Engineer joins the Reference Data Architecture team within the global Information Research group to build and operationalize software capabilities that expose semantic assets through Enterprise Ontology Services. This role involves designing and building AI-driven solutions, translating ontologies, controlled vocabularies, and reference standards into machine-consumable services, APIs, MCP tools, agents, and reusable components. The engineer will work on projects like the Ontoverse initiative to create a semantic layer for data integration, inference, and discovery. Key technical requirements include proficiency in Python libraries such as Pydantic, REST APIs, XML, JSON, and knowledge of OWL, RDF, SPARQL, and FAIR data principles. The role addresses the challenge of making complex internal and external information more connected and actionable by integrating semantic architecture with GenAI technologies to support large-scale enterprise workflows and decision-making processes.
Build and operationalize AI-driven semantic solutions that integrate enterprise ontologies into core data platforms.
Translate ontologies, controlled vocabularies, and reference standards into machine-consumable APIs, agent tools, and reusable software components.
Enable semantic structures to drive inference, search, normalization, and entity resolution across large-scale workflows.
Define implementation patterns and quality gates to embed FAIR data principles into generated software assets.
Develop technical documentation and integration patterns to help teams consume semantic services consistently.
Maintain CI/CD practices to ensure reliable delivery and ongoing operational support for semantic tools.
What we're looking for
Bachelor's degree in Data Science, Computer Science, Informatics, Software Engineering, Semantic Technologies, or a related field and 5 years of experience; or a Master's degree and 4 years of experience.
Experience building production-quality data, software, AI, or semantic technology solutions including APIs, MCP tools, agents, services, integrations, and reusable platform components.
Proficiency in common technologies and formats such as REST APIs, XML, JSON, and Python libraries like Pydantic.
Working knowledge of ontologies, controlled vocabularies, knowledge graphs, metadata, mapping, normalization, and FAIR data principles.
Knowledge of semantic standards including OWL, RDF, and SPARQL.
Ability to translate business and governance requirements into scalable technical designs for AI agents, search, and data integration workflows.
Ability to produce clear technical documentation and contribute to CI/CD practices for reliable delivery and operational support.
Ontologies
Knowledge Graphs
RDF
OWL
Semantic Web
RAG
Generative AI
Agentic AI
Metadata Management
Graph Databases
Data Science
Machine Learning
Deep Learning
Data Governance
Statistical Analysis
Time Series Analysis