Ontology Engineer - Applied AI & Data Platforms

Applico Capital is the leading venture capital firm focused on the $8 trillion B2B distribution industry. Through our learnings and understanding of the industry, we are building a tech startup, currently in stealth, to solve the industry's biggest problems as it comes to unlocking AI-enabled synergies.

Our mandate is to leverage AI and modern technologies to reimagine the role of the traditional distributor and transform how the entire industry operates.

We are looking for highly technical builders who thrive in entrepreneurial, scrappy, and collaborative environments.

We’re seeking an Ontology Engineer to help design and implement the semantic and structural backbone of this new data platform.

You’ll model how products, suppliers, customers, and transactions interconnect — defining the schemas, relationships, and standards that make enterprise data interoperable, trustworthy, and ready for automation and AI.

This role sits at the intersection of data architecture, knowledge representation, and applied AI. You’ll work closely with data engineers, architects, and AI teams to ensure every dataset aligns to a consistent, machine-understandable model that scales across multiple distributors and business systems.

• Design and maintain domain ontologies and semantic schemas that describe key business entities (e.g., product, supplier, customer, location, transaction)
• Map and align internal data structures (ERP, CRM, PIM, CMS) to open data standards and taxonomies (e.g., schema.org, GS1, UNSPSC, ETIM)
• Build and manage knowledge graph models and associated metadata structures
• Define and enforce data modeling conventions, relationships, and validation rules (RDF, OWL, SHACL, JSON-LD)
• Partner with data engineers and architects to embed semantic models into pipelines and APIs
• Contribute to data governance, lineage, and interoperability practices across business domains
• Collaborate with AI teams to support contextual retrieval, entity linking, and enrichment using LLMs and embeddings
• Evaluate and adopt open-source tools for ontology management, graph storage, and reasoning (e.g., Protégé, Neo4j, Stardog, Weaviate, ArangoDB)
• Help design developer-facing documentation and schemas to make complex data understandable and reusable

• 5+ years of experience in ontology engineering, data modeling, or knowledge graph development
• Proficiency with semantic web standards and technologies (RDF, OWL, SHACL, JSON-LD, SPARQL)
• Experience designing or maintaining domain ontologies or taxonomies for real-world business applications
• Familiarity with enterprise data integration (ERP, CRM, PIM, MDM, CMS) and how to model heterogeneous data
• Knowledge of open data standards such as schema.org, GS1, ETIM, UNSPSC, or eCl@ss
• Hands-on experience with graph databases (Neo4j, ArangoDB, Weaviate, Ontotext GraphDB, or similar)
• Strong understanding of data lineage, governance, and interoperability principles
• Experience collaborating with data engineers and AI/ML teams
• Bonus: exposure to vector search, RAG pipelines, or LLM-based enrichment
• Excellent communication skills — able to explain semantic concepts to non-technical audiences

• Build the semantic foundation powering one of the most ambitious AI and data transformations in B2B commerce
• Work alongside experts in data architecture, AI engineering, and automation
• Help define how modern enterprises structure and share data in an open, extensible way
• Operate with startup agility inside a large, stable, and forward-thinking business

• Standards: RDF, OWL, SHACL, JSON-LD, schema.org, GS1, ETIM
• Databases: Neo4j, Weaviate, ArangoDB, Ontotext GraphDB, Stardog
• Languages: Python, SPARQL, SQL
• Tools: Protégé, dbt, Airbyte, Dagster/Prefect
• AI & Enrichment (Optional): LangChain, LangGraph, DSPy, OpenAI APIs