Senior Consultant – Semantic Data & AI Engineering

Capco•New York, NY

3d•$114,000 - $143,000•Hybrid

About The Position

Capco is seeking a hands-on Senior Consultant to design and deliver semantic data solutions that make enterprise information usable by artificial intelligence, machine learning, analytics, and business applications. This role is ideal for a versatile consultant who can contribute across knowledge-graph development, AI solution engineering, semantic modeling, and data-pipeline delivery. Depending on project needs, you may work as a knowledge-graph engineer, semantic modeler, AI engineer, data engineer, technical analyst, or workstream lead. You will collaborate with client stakeholders, architects, data scientists, AI engineers, and data engineers to connect structured and unstructured information, create machine-understandable knowledge models, and deliver trusted data foundations for AI. This is a hands-on delivery role requiring participation in modeling, coding, integration, testing, documentation, and production implementation.

Requirements

Four to six years of experience in data engineering, software engineering, artificial intelligence, analytics, knowledge management, or a related technology discipline.
Two or more years of hands-on experience with knowledge graphs, semantic technologies, graph databases, semantic-data integration, or closely related solutions.
Working knowledge of semantic standards such as RDF, RDFS, OWL, SPARQL, SHACL, SKOS, JSON-LD, or Turtle.
Experience with at least one graph platform such as Stardog, Neo4j, GraphDB, Amazon Neptune, Anzo, MarkLogic, TigerGraph, Apache Jena, TypeDB, or an equivalent technology.
Proficiency in Python, Java, or another enterprise programming language.
Experience developing data pipelines, transformations, APIs, automated tests, or production integrations.
Working knowledge of NLP, machine learning, embeddings, vector search, semantic search, RAG, GraphRAG, or LLM-based applications.
Experience integrating structured data with unstructured content such as policies, contracts, research, communications, or operational documents.
Familiarity with one or more cloud or modern data platforms, including Microsoft Azure, AWS, Google Cloud, Microsoft Fabric, Databricks, Snowflake, BigQuery, or Redshift.
Understanding of relational, graph, document, vector, and lakehouse data architectures.
Familiarity with Git, CI/CD, automated testing, containers, Agile delivery, and production-support practices.
Strong analytical, problem-solving, documentation, and communication skills.
Ability to work across multiple technical roles, learn unfamiliar technologies, and contribute throughout the delivery lifecycle.

Nice To Haves

Experience in financial services or another regulated industry is preferred.
Familiarity with data governance, metadata management, entity resolution, master data, responsible AI, model risk, regulatory reporting, or data privacy is advantageous.
A bachelor’s degree or equivalent professional experience in computer science, data science, information systems, engineering, linguistics, mathematics, or a related discipline is preferred.
Relevant cloud, data-engineering, AI/ML, Agile, or graph-technology certifications are a plus.

Responsibilities

Design and build knowledge graphs, semantic layers, ontologies, taxonomies, and graph-based data products.
Translate business concepts, data models, policies, documents, and subject-matter expertise into machine-readable semantic models.
Develop data pipelines that acquire, transform, map, validate, enrich, and load information from databases, APIs, files, documents, and cloud platforms.
Integrate knowledge graphs with generative AI, retrieval-augmented generation, GraphRAG, semantic search, machine learning, and intelligent-agent solutions.
Support NLP and document-intelligence use cases, including entity extraction, entity linking, relationship extraction, classification, natural language inference, and knowledge extraction.
Combine graph data, metadata, vector search, business rules, and model outputs to improve AI grounding, accuracy, explainability, and traceability.
Develop Python- or Java-based data transformations, APIs, services, validation routines, and integration components.
Prepare and manage data used for AI retrieval, model evaluation, inference, and analytics.
Implement semantic-data quality controls, including SHACL validation, provenance, lineage, confidence scoring, and version management.
Participate in graph-platform evaluations, proofs of concept, architecture decisions, performance testing, and production deployments.
Create automated tests and support CI/CD, monitoring, troubleshooting, and production-support activities.
Facilitate requirements and modeling sessions with business and technical stakeholders.
Produce technical designs, semantic models, mappings, test cases, deployment documentation, and operational procedures.
Lead defined technical workstreams and mentor junior consultants, engineers, and analysts.
Contribute to reusable solution patterns, demonstrations, accelerators, proposals, and client presentations.