Semantic Data & AI Engineering
Remote
Long Term
Contract
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 technical concepts clearly to business stakeholders, architects, engineers, data scientists, and executives.
Demonstrated ability to operate across multiple roles, learn new technologies quickly, and take ownership from initial discovery through production delivery.
Munesh
,
CYBER SPHERE LLC
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