This role pays more than
64%
of similar roles. Most pay
$223,087–$297,000
— the shaded band above.
At the midpoint, this role pays about
$278k
versus about
$260k
for comparable roles.
Based on 240 similar postings.
Employer
About Novartis
Novartis is a global biopharmaceutical company that researches, develops, manufactures, and markets prescription drugs in areas including oncology, immunology, neuroscience, and cardiology. Industry: Biopharmaceuticals
Novartis currently has
26 open roles
on FindRole.
Listed pay typically runs
$194,600–$361,400
across 26 roles with salary data.
The Director, Semantic & Knowledge Engineering leads the design, implementation, and governance of enterprise semantic models, ontologies, knowledge graphs, and metadata frameworks that power NovaOS. Reporting to the Executive Director, this role involves building trusted semantic assets to enable interoperable data, AI reasoning, retrieval-augmented generation (RAG), and scalable enterprise intelligence. You will develop and maintain knowledge graphs and semantic services while establishing metadata standards and quality controls for enterprise knowledge assets. The role requires collaborating with Applied AI, Data Science, Product, and Engineering teams to integrate these capabilities into products. Key technical requirements include expertise in RDF/OWL, graph databases, and semantic web technologies. The work focuses on providing the foundational architecture for agentic AI, reasoning engines, and contextual search within complex environments such as pharmaceutical or healthcare industries where high-quality data interoperability is essential.
Lead the design and implementation of enterprise ontologies, taxonomies, business vocabularies, and semantic models.
Develop and maintain knowledge graphs and semantic services to support AI, analytics, search, and business applications.
Integrate semantic capabilities into NovaOS products by partnering with AI, Data Science, Product, and Engineering teams.
Establish metadata standards, semantic governance processes, and quality controls for enterprise knowledge assets.
Build semantic foundations for RAG, agentic AI, reasoning engines, and contextual search.
Evaluate emerging semantic technologies and recommend improvements to the enterprise knowledge architecture.
Lead and mentor semantic and knowledge engineers while promoting reusable engineering patterns and technical excellence.
What we're looking for
Bachelor's or advanced degree in Computer Science, Information Science, Artificial Intelligence, Data Science, Bioinformatics, or a related discipline.
8+ years of experience in semantic technologies, knowledge engineering, metadata management, or enterprise information architecture.
Hands-on expertise with ontologies, RDF/OWL, knowledge graphs, graph databases, and semantic web technologies.
Experience implementing semantic solutions for AI, analytics, interoperability, and enterprise search.
Strong leadership, communication, and stakeholder management skills to lead cross-functional technical initiatives.
Experience applying semantic technologies to generative AI, agentic AI, RAG, and enterprise AI platforms (preferred).
Experience in pharmaceutical, healthcare, or other highly regulated industries (preferred).
Knowledge Graphs
RDF
OWL
Semantic Web
RAG
Ontologies
Taxonomies
Metadata Management
Data Governance
Machine Learning
Deep Learning
Artificial Intelligence
Graph Databases
Data Science
Statistical Analysis
Time Series Analysis
Data Visualization
Agentic AI
LLMs
RAG
Python
Vector Databases
Machine Learning
Deep Learning
AI Orchestration
Cloud Platforms
APIs
Data Science
Graph Algorithms
Time Series Analysis
Statistical Analysis
Data Visualization