Principal Consultant – Semantic Data & AI Engineering
On-siteNew York, United States
Job Summary
Design and implement enterprise knowledge graphs, semantic layers, and ontologies that translate business concepts into machine-readable models. Build scalable pipelines to acquire, transform, map, validate, and load data while integrating these structures with generative AI, vector search, and GraphRAG solutions. Develop Python- or Java-based services, APIs, and integration components to support semantic workflows, including NLP use cases for entity and relationship extraction. Define data quality controls, provenance, lineage tracking, and governance processes to ensure integrity across complex data ecosystems. This role requires 10–15 years of experience in data engineering and semantic technologies, with deep expertise in RDF, SPARQL, and graph databases like Neo4j or Amazon Neptune. Candidates must demonstrate proficiency in Python and Java, cloud platforms (Azure, AWS, Google Cloud), and DevOps practices within an Agile environment.
Required Qualifications
- 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
Hiring someone like this?
Get your role in front of qualified candidates on Sorce.