Senior AI Data Engineer

Responsibilities:

Semantic Layer Implementation
• Implement JSON-LD-based semantic models designed by the ontologist into production data systems
• Build and maintain knowledge graph structures that reflect canonical domain models • Develop and manage graph database schemas, queries, and data ingestion pipelines
• Ensure semantic consistency between ontology definitions and downstream data product 
AI & Vector Infrastructure
• Design and implement embedding pipelines that represent Comply’s financial and regulatory data in
  vector space
• Build and operate vector database infrastructure for semantic search and similarity retrieval
• Implement RAG (Retrieval-Augmented Generation) architectures that ground LLM outputs in Comply’s
  proprietary data
• Evaluate and integrate LLM tooling and frameworks appropriate to Comply’s use cases
Data Pipeline & Platform Engineering
• Build reliable, observable data pipelines that feed the semantic layer from upstream broker and
  regulatory data sources
• Apply DataOps practices including testing, monitoring, lineage tracking, and SLAs
• Work with Data Engineers and Backend Engineers to embed semantic models into APIs and data contracts
• Ensure the semantic layer scales with data volume and platform growth
Collaboration & Enablement
• Partner closely with the Ontologist to ensure implemented models faithfully reflect domain intent
• Support consuming application teams in understanding and adopting AI-ready data products
• Contribute to resolving cross-domain data integration challenges

Skills and Qualifications:

• Strong hands-on experience in data engineering, with a focus on semantic or AI data infrastructure
• Experience building and operating knowledge graphs or graph databases (e.g. Jena Fuseki, Neo4j, Amazon
  Neptune, or equivalent)
• Experience with vector databases and embedding pipelines (e.g. Pinecone, Weaviate, Qdrant, pgvector)
• Practical experience implementing RAG architectures or LLM-integrated data pipelines
• Familiarity with semantic web standards — JSON-LD, RDF, OWL, or SKOS
• Strong Python skills and experience with data pipeline frameworks
• Experience with cloud-native data platforms (AWS, Azure, or GCP)
• Exposure to domain-driven design (DDD) and bounded contexts is desirable.
• Experience working directly with ontologists or knowledge engineers is a plus. 
• Familiarity with data contracts and data product frameworks is a plus. 
• Experience with DataOps tooling, data reliability, or data observability platforms is desirable. 
• Background in financial services, RegTech, or compliance data is a  plus.