Ontology & Semantic Modeler, Data Context Layer

Merck Merck · Pharma · Telangana, India

Seeking an Ontology and Semantic Modeler to design, develop, and maintain the semantic foundations of an enterprise platform (Data Context Layer - DCL). This role will create and govern ontologies and semantic models to provide consistent, reusable, and machine-readable context across systems and AI-enabled workflows, specifically enabling agentic AI capabilities by providing structured context and semantic grounding for intelligent agents. Requires strong experience in ontology engineering, semantic modeling, OWL/RDF, knowledge representation, and familiarity with agentic AI, prompt engineering, and context engineering.

What you'd actually do

  1. Design and maintain enterprise ontologies and semantic models that represent key business concepts, entities, relationships, and rules.
  2. Define modeling standards, conventions, and lifecycle practices for semantic assets across the enterprise.
  3. Collaborate with product, architecture, engineering, governance, and domain experts to translate business needs into formal semantic structures.
  4. Ensure semantic models support standardized, self-service serving of data context.
  5. Develop and maintain OWL/RDF-based models, vocabularies, taxonomies, and controlled terminologies as needed.

Skills

Required

  • 5+ years of experience in ontology modeling, semantic modeling, information modeling, or knowledge representation.
  • Strong knowledge of OWL, RDF, ontologies, controlled vocabularies, and semantic web concepts.
  • Experience building semantic models for enterprise data platforms, knowledge graphs, or metadata-driven systems.
  • Familiarity with agentic AI, prompt engineering, and context engineering concepts.
  • Ability to translate complex business concepts into formal model structures.
  • Strong collaboration skills and experience working with domain experts, architects, and engineers.
  • Experience defining semantic standards and supporting governance processes.

Nice to have

  • Experience with ontology management tools or semantic platforms such as timbr, protege or similar technologies.
  • Familiarity with MCP, A2A, knowledge graphs, semantic layers, or ontology-driven data access.
  • Background in enterprise data governance, master data, metadata management, or taxonomy development.
  • Experience suppo

What the JD emphasized

  • agentic AI
  • semantic grounding
  • structured context
  • ontology engineering
  • semantic modeling
  • OWL/RDF
  • knowledge representation
  • context engineering
  • prompt engineering

Other signals

  • design and develop semantic foundations for an enterprise platform
  • enabling agentic AI capabilities
  • structured context and semantic grounding for intelligent agents
  • publish, discover, and consume standardized data context through self-service
  • creating and governing ontologies, semantic models, and related standards