Senior AI Data Engineer: Semantic Graphs & LLM Pipelines

Who Are We:

Comply
is the leading provider of compliance SaaS and consulting services for the global financial services sector. With more than 5,000 clients and hundreds of employees across the globe, Comply empowers Chief Compliance Officers and their teams to proactively manage regulatory obligations, mitigate risk, and scale with efficiency and confidence.

Comply serves thousands of global financial services clients including broker-dealers, insurers, investment banks, private funds, RIAs, and wealth managers who rely on Comply offerings to power their compliance programs.

To learn more about Comply, visit

The Role:

We are looking for Senior AI Data Engineers to implement and operationalize Comply’s semantic layer — turning the ontological models defined by our ontologist and architects into working knowledge graphs, vector search infrastructure, and LLM-powered pipelines. This is a hands-on engineering role at the intersection of knowledge representation, AI infrastructure, and data platform engineering. You will own the delivery of semantic layer components, collaborate closely with application and data engineering teams, and ensure that AI-ready data products are reliable, performant, and adopted in practice.

You will report into the Data and Analytics organization as part of a new team being created to enable future data capabilities in relation to our AI ambitions.

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 Data Ops 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

• 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…