NLP Solution Engineer

June 7

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Description

• Research state-of-the art approaches and toolkits for solving problems related to semantic and ontology domain. • Propose methodologies covering various text/image/graph/chart analysis challenges. • Lead the strategy, design and development of enterprise semantic solutions (metadata, taxonomies, ontologies, knowledge graphs, etc.). • Lead the implementation of semantic layer solutions, such as taxonomy/ontology management tools, graph databases/triple stores, labeled property graphs (LPGs), etc. • Interpret existing semantic schemas and help to develop and deliver the design and implementation of solutions architectures and governance plans. • Play a key role in ensuring the quality, interoperability, and reusability of our data and knowledge assets, following the FAIR Data Principles and the Semantic Web standards.

Requirements

• At least 3 years designing and managing taxonomies, thesauri, and/or ontologies. • Expertise in knowledge engineering, knowledge representation, ontology development, and in the use of generative AI and LLMs in complex scientific domains. • Expertise in the FAIR Data Principles, the Semantic Web technology stack (SCHACL, RDF, RDF*, OWL, etc.), and experience using ontology development tools. • Driven by a strong belief in the business applications of knowledge graphs and ability to adapt to new technology. • Experience using semantic standards like RDF, OWL, SKOS, and SPARQL. • Programming experience (e.g., Java, C/C++, Python). • Experience/knowledge in data modeling, NLP, pattern recognition, knowledge graphs. • Experience in natural language processing tools and platforms (such as spaCy) and computational linguistics will be considered as a strong advantage. • Knowledge of cloud platforms (AWS, Azure, GCP) and containerization (Docker, Kubernetes). • Experience with SQL and NoSQL databases. • Understanding of web technologies (HTML, CSS, JavaScript).