Principal Consultant – Semantic Data & AI Engineering

Capco | Data & Analytics | New York, NY About The Role Capco is seeking a hands-on Principal Consultant to design and build semantic data solutions that make enterprise data usable by artificial intelligence, machine learning, analytics, and business applications.

This role is designed for a versatile technical leader who can move across knowledge-graph engineering, AI solution development, data modeling, and data-pipeline delivery. The successful candidate will help clients connect structured and unstructured information, create machine-understandable representations of business knowledge, and provide trusted context for AI applications.

You will work across multiple project roles depending on client needs—serving as a semantic architect, knowledge-graph engineer, AI engineer, data engineer, technical lead, or client advisor. This is not a research-only or ontology-only position. The role requires someone who can translate business requirements into practical solutions and contribute directly to architecture, code, data pipelines, testing, and production delivery.

What You’ll Do

• Design and implement enterprise knowledge graphs, semantic layers, ontologies, taxonomies, and graph-based data products.
• Translate business concepts, policies, documents, data models, and subject-matter expertise into governed, machine-readable knowledge models.
• Build semantic data pipelines that acquire, transform, map, validate, enrich, and load data from databases, APIs, files, documents, events, and cloud platforms.
• Integrate knowledge graphs with AI and machine-learning solutions, including generative AI, retrieval-augmented generation, GraphRAG, semantic search, and intelligent agents.
• Support NLP and document-intelligence use cases such as entity extraction, entity linking, relationship extraction, classification, natural language inference, and knowledge extraction.
• Combine graph traversal, vector search, metadata, rules, and model-generated results to improve AI accuracy, grounding, explainability, and traceability.
• Develop Python- or Java-based services, data transformations, APIs, validation routines, and integration components.
• Work with data scientists and AI engineers to prepare training, retrieval, evaluation, and inference data.
• Work with data engineers to implement batch, streaming, and API-driven pipelines using modern cloud and data platforms.
• Define semantic-data quality controls, provenance, lineage, confidence scoring, versioning, and governance processes.
• Evaluate graph databases, vector databases, data platforms, AI frameworks, and cloud services based on client requirements.
• Lead technical workshops, architecture decisions, prototypes, and production implementations.
• Mentor team members and create reusable patterns, accelerators, and reference architectures for Capco’s Graph, Semantics & AI practice.
• Support proposals, solution estimates, client presentations, and the development of new consulting offerings.

What You’ll Bring

• Eight or more years of experience in data engineering, software engineering, artificial intelligence, analytics, enterprise architecture, or a related field.
• At least four years of hands-on experience with knowledge graphs, semantic technologies, graph databases, or semantic-data integration.
• Strong knowledge of RDF, RDFS, OWL, SPARQL, SHACL, SKOS, JSON-LD, Turtle, or related standards.
• Experience with one or more graph platforms such as Stardog, Neo4j, GraphDB, Amazon Neptune, Anzo, MarkLogic, TigerGraph, Apache Jena, TypeDB, or equivalent technologies.
• Strong programming skills in Python, Java, or a comparable enterprise language.
• Experience developing data pipelines, APIs, transformations, automated tests, and production integrations.
• Working knowledge of NLP, machine learning, embeddings, vector search, semantic search, RAG, GraphRAG, or LLM-based applications.
• Experience connecting structured data with unstructured content such as policies, contracts, research, communications, or operational documents.
• Familiarity with cloud and modern data platforms such as Microsoft Azure, AWS, Google Cloud, Microsoft Fabric, Databricks, Snowflake, BigQuery, or Redshift.
• Understanding of relational, graph, document, vector, and lakehouse architectures and when to use each.
• Experience with Git, CI/CD, automated testing, containers, Agile delivery, and production-support practices.
• Ability to communicate technic…