Company Overview :

Whitewhale Software Solutions is a specialized technology firm focused on building advanced data intelligence platforms and enterprise-grade software architectures. The company operates at the intersection of complex data engineering and artificial intelligence, providing scalable solutions that enable organizations to derive actionable insights from unstructured and semi-structured data. With a culture rooted in technical excellence and innovation, Whitewhale serves a diverse global clientele, helping businesses modernize their data infrastructure and implement sophisticated knowledge management systems.

Role Overview :

As a Semantic Architect, you will serve as the technical lead for designing and implementing complex knowledge structures that power our AI-driven applications. You will bridge the gap between raw data and intelligent systems by architecting robust ontologies and knowledge graphs that facilitate advanced reasoning and information retrieval.

Working closely with data scientists, software engineers, and product stakeholders, you will ensure that our data models are not only technically sound but also aligned with business objectives. Your work will directly influence the accuracy of NLU/NLG outputs and the overall efficiency of our data governance frameworks, ultimately shaping how our clients interact with their internal knowledge assets.

Key Responsibilities :

- Design and maintain enterprise-grade ontologies and taxonomies to structure complex domain knowledge for AI and machine learning applications.

- Develop and optimize semantic models using RDF and OWL to ensure interoperability and logical consistency across disparate data sources.

- Architect and manage high-performance graph databases using Neo4j to support real-time querying and complex relationship mapping.

- Execute advanced SPARQL queries to extract meaningful insights and validate the integrity of the knowledge graph.

- Implement comprehensive data governance policies to ensure the quality, security, and lifecycle management of metadata across the organization.

- Collaborate with NLU/NLG engineering teams to integrate semantic layers into natural language processing pipelines, enhancing the contextual understanding of AI models.

- Lead the technical strategy for data modeling initiatives, ensuring that architectural decisions scale effectively with increasing data volume and complexity.

Required Skillset :

- Demonstrated expertise in Ontology Engineering and Semantic Modeling, with a deep understanding of W3C standards including RDF, OWL, and SPARQL.

- Proven ability to design and deploy Graph DBMS solutions, specifically Neo4j, in production environments.

- Strong proficiency in Python for data manipulation, automation, and integrating semantic layers with broader software stacks.

- Skilled in developing structured taxonomies and metadata frameworks that improve data discoverability and organizational knowledge sharing.

- Exceptional analytical abilities to translate complex business requirements into logical, machine-readable data models.

- Strong communication skills to articulate technical architectural concepts to non-technical stakeholders and cross-functional teams.

- Ability to thrive in a fully remote environment, demonstrating self-motivation, clear documentation habits, and effective collaboration across distributed teams.

- A minimum of 7 to 10 years of professional experience in data architecture, knowledge engineering, or a related field, with a solid foundation in Artificial Intelligence principles.