Our client is seeking a Principal Consultant – Semantic Data & AI Engineering with deep expertise in semantic data technologies, knowledge graphs, and AI engineering to design and implement enterprise-scale semantic solutions. This role combines hands-on technical leadership with strategic guidance on knowledge-graph architecture, AI integration, and data governance.
Responsibilities & Qualifications
Design and implement enterprise knowledge graphs, semantic layers, ontologies, taxonomies, and graph-based data products that translate business concepts into machine-readable models
Build semantic data pipelines that acquire, transform, map, validate, enrich, and load data at scale
Integrate knowledge graphs with AI and machine-learning solutions, including generative AI, vector search, and GraphRAG implementations
Develop Python- or Java-based services, APIs, data transformations, and integration components to support semantic workflows
Support NLP and document-intelligence use cases including entity extraction, relationship extraction, and semantic enrichment
Define and implement semantic data quality controls, data provenance, lineage tracking, and governance processes
Evaluate and recommend appropriate graph databases, vector databases, and AI frameworks based on client requirements
Lead technical workshops, architecture decisions, and mentor team members on semantic design patterns and best practices
Requirements
10–15 years of professional experience in data engineering, semantic technologies, and AI/ML systems
Demonstrated expertise in knowledge graphs, RDF, RDFS, OWL, SPARQL, SHACL, SKOS, and JSON-LD
Hands-on experience with graph databases such as Neo4j, Stardog, GraphDB, Amazon Neptune, or equivalent platforms
Strong proficiency in Python and Java for building data pipelines, services, and integrations
Solid understanding of NLP, machine learning, vector search, RAG, and LLM applications
Experience with cloud platforms (Azure, AWS, or Google Cloud) and DevOps practices including Git and CI/CD
Comfort with Agile methodologies and cross-functional collaboration with data scientists, architects, and business stakeholders