Knowledge Engineer

A Knowledge Engineer designs, builds, and maintains knowledge-based systems that enable organizations to capture, structure, and reuse expertise. This role sits at the intersection of artificial intelligence, data engineering, and domain-specific knowledge management. Knowledge Engineers work closely with subject matter experts to elicit tacit knowledge and transform it into formal representations such as ontologies, knowledge graphs, rule-based systems, or semantic models. They are responsible for the entire lifecycle of knowledge assets, from acquisition and modeling to deployment and maintenance.

Typical responsibilities include developing ontologies using standards like RDF, OWL, and SKOS; writing SPARQL queries to extract insights from knowledge graphs; integrating knowledge bases with machine learning pipelines for hybrid AI systems; and building expert systems or decision-support tools. Knowledge Engineers often collaborate with data scientists, software engineers, and product managers to embed knowledge capabilities into larger applications. They may also evaluate and select knowledge management platforms, such as graph databases (Neo4j, GraphDB) or semantic middleware (Apache Jena, TopBraid).

Seniority signals: Junior Knowledge Engineers focus on data modeling and ontology maintenance under guidance; Mid-level engineers lead knowledge acquisition sessions and design small-to-medium ontologies; Senior engineers architect enterprise-scale knowledge graphs, define governance policies, and drive strategic adoption of knowledge technologies. The role is highly compatible with remote and hybrid work, as knowledge engineering tasks are largely digital and collaborative via version-controlled repositories (e.g., Git for ontologies) and virtual whiteboarding tools.

Salary drivers include industry (AI-first companies, healthcare, finance, and research pay premium), depth of technical stack (e.g., experience with NLP, reasoning engines, or graph algorithms), and ability to bridge business and technical domains. Common stack signals: Protégé, TopBraid, GraphDB, Neo4j, Apache Jena, Elasticsearch, Python, Java, and semantic web standards. Certifications such as Certified Knowledge Manager (CKM) or TOGAF can differentiate candidates. The role is growing with the rise of knowledge graphs in enterprise AI and semantic search.