Job Description

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