Senior AI Data Engineer — Semantic Layer & Vector AI Infra

Comply; Greater London; On-site; GBP 90,000 - 130,000; Full time

Job summary

Comply is seeking Senior AI Data Engineers to operationalize its semantic layer—translating ontological models into working knowledge graphs, vector search infrastructure, and LLM-powered pipelines.

You will own semantic layer components, collaborate with application and data teams, and ensure AI-ready data products are reliable, performant, and adopted across the organization. This hands-on role sits within the Data and Analytics organization to enable future data capabilities for AI ambitions.

Qualifications

• Hands-on data engineering with semantic or AI data infrastructure.
• Experience building knowledge graphs or graph databases (e.g., Neo4j, Neptune).
• Experience with vector databases and embedding pipelines (e.g., Pinecone, Weaviate, Qdrant).
• Experience implementing RAG architectures or LLM-integrated data pipelines.

Responsibilities

• Implement JSON-LD based semantic models into production data systems.
• Build and maintain knowledge graph structures reflecting canonical domain models.
• Develop and manage graph DB schemas, queries, and data ingestion pipelines.
• Ensure semantic consistency between ontology definitions and downstream data products.
• Design and operate embedding pipelines and vector DB infrastructure for semantic search.

Skills

Semantic data engineering

Knowledge graphs

Python

Data pipelines

Cloud data platforms

DataOps

RAG architectures

APIs & data contracts

Domain-driven design

Collaboration with ontologists

Education

Bachelor's degree in CS/IS

Tools

Neo4j

Jena Fuseki

Amazon Neptune

Weaviate

pgvector

Pinecone

Qdrant

Job description

Comply is seeking Senior AI Data Engineers to operationalize its semantic layer—translating ontological models into working knowledge graphs, vector search infrastructure, and LLM-powered pipelines.

You will own semantic layer components, collaborate with application and data teams, and ensure AI-ready data products are reliable, performant, and adopted across the organization. This hands-on role sits within the Data and Analytics organization to enable future data capabilities for AI ambitions.