<p>We are seeking a meticulous Knowledge Engineer to develop, curate and maintain enterprise knowledge assets. The role bridges data science, ontology engineering and applied natural language processing. You will collaborate with subject matter experts and engineers to convert organisational information into reliable, reusable knowledge. The post requires strong analytical skills, attention to provenance and a practical approach to delivering production solutions.</p>
<h2>Knowledge Engineer Job Profile</h2>
<p>The Knowledge Engineer designs, models and implements knowledge graphs, ontologies and taxonomies to support search, question answering and decision support systems. They transform unstructured and structured data into consistent, queryable knowledge models.</p>
<p>They author mappings, validate inference rules and ensure data quality, provenance and scalability across knowledge pipelines. The role involves close collaboration with data engineers, ML teams and business stakeholders.</p>
<h2>Knowledge Engineer Job Description</h2>
<p>The Knowledge Engineer is responsible for the end to end lifecycle of knowledge assets. This includes requirements gathering with domain teams, ontology and schema design, data extraction and curation, and integration of knowledge stores with applications. You will develop and maintain semantic models that enable precise information retrieval and reliable automated reasoning.</p>
<p>Day to day tasks include building entity and relation extraction routines, creating and validating alignments between vocabularies, and implementing SPARQL endpoints or API layers for consumption. The role demands pragmatic decisions about trade offs between expressivity and performance, and a commitment to reproducible pipelines and testable artefacts.</p>
<p>The successful candidate will monitor and improve knowledge quality using metrics and tooling, such as consistency checks, provenance tracking and version control. You will also author documentation and guide stakeholders in the adoption of knowledge-driven features. Familiarity with graph databases, RDF, OWL and contemporary NLP toolkits is essential, as is an ability to translate business concepts into formal representations.</p>
<h2>Knowledge Engineer Duties and Responsibilities</h2>
<ul>
<li>Design and implement ontologies, taxonomies and knowledge graphs that reflect business domain models.</li>
<li>Extract, cleanse and map data from diverse sources into standardised knowledge representations.</li>
<li>Develop and maintain pipelines for entity and relation extraction using NLP techniques.</li>
<li>Author and maintain SPARQL queries, APIs and integration layers for knowledge consumption.</li>
<li>Ensure data quality, provenance and versioning of knowledge assets.</li>
<li>Perform ontology alignment and vocabulary mapping across systems.</li>
<li>Collaborate with data engineers, ML researchers and product teams to productionise solutions.</li>
<li>Create tests, validation rules and monitoring to measure knowledge reliability.</li>
<li>Document models, processes and governance to aid reuse and compliance.</li>
<li>Provide technical guidance and training to domain experts and peers.</li>
</ul>
<h2>Knowledge Engineer Requirements and Qualifications</h2>
<ul>
<li>Bachelor's or master's degree in Computer Science, Linguistics, Information Science or related field.</li>
<li>Proven experience in knowledge engineering, ontology development or semantic web projects.</li>
<li>Practical knowledge of RDF, OWL, SPARQL and graph databases such as Neo4j or Blazegraph.</li>
<li>Experience with NLP tools and frameworks for entity recognition and relation extraction.</li>
<li>Familiarity with Python and libraries for data processing and machine learning.</li>
<li>Strong analytical skills and experience with data modelling and schema design.</li>
<li>Understanding of data governance, provenance and ethical considerations in knowledge work.</li>
<li>Excellent communication skills to work effectively with technical and non technical stakeholders.</li>
<li>Experience with version control, CI pipelines and reproducible workflows.</li>
<li>Ability to prioritise tasks and deliver pragmatic solutions in a fast paced environment.</li>
</ul>