EI
Semantic Data & AI Engineer
Expert In Recruitment Solutions
🇺🇸 United States
On-site
Staff / Principal
3 weeks ago
- AI
- Machine Learning
- Data Modeling
- Natural Language Processing
- Vector Search
- Python
- Java
- JSON
- Neo4j
- Neptune
- Apache
- RAG
- Azure
- AWS
- GCP
- Microsoft Fabric
- Databricks
- Snowflake
- BigQuery
- Redshift
- Git
- CI/CD
- Agile
- AI/ML
3 weeks ago
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.
- 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.
Financial-services or other regulated-industry experience is strongly preferred. Experience with data governance, metadata management, entity resolution, master data, responsible AI, model risk, or regulatory reporting is advantageous.
Relevant cloud, data-engineering, AI/ML, Agile, or graph-technology certifications are a plus.
Semantic Data & AI Engineer · Expert In Recruitment Solutions