Asks for

The ad says“Experience building and operating knowledge graphs or graph databases (e.g., Jena Fuseki, Neo4j, Amazon Neptune, or equivalent).”
The ad says“Experience with cloud-native data platforms (AWS, Azure, or GCP).”
The ad says“Experience with cloud-native data platforms (AWS, Azure, or GCP).”
The ad says“Strong hands-on data engineering experience, with a focus on semantic or AI data infrastructure.”
The ad says“Experience with vector databases and embedding pipelines (e.g., Pinecone, Weaviate, Qdrant, pgvector).”
The ad says“Experience with cloud-native data platforms (AWS, Azure, or GCP).”
The ad says“Experience building and operating knowledge graphs or graph databases (e.g., Jena Fuseki, Neo4j, Amazon Neptune, or equivalent).”
The ad says“Experience building and operating knowledge graphs or graph databases (e.g., Jena Fuseki, Neo4j, Amazon Neptune, or equivalent).”
The ad says“Familiarity with semantic web standards—JSON-LD, RDF, OWL, or SKOS.”
The ad says“Experience building and operating knowledge graphs or graph databases (e.g., Jena Fuseki, Neo4j, Amazon Neptune, or equivalent).”
The ad says“Experience building and operating knowledge graphs or graph databases (e.g., Jena Fuseki, Neo4j, Amazon Neptune, or equivalent).”
The ad says“Familiarity with semantic web standards—JSON-LD, RDF, OWL, or SKOS.”
The ad says“Experience with vector databases and embedding pipelines (e.g., Pinecone, Weaviate, Qdrant, pgvector).”
The ad says“Experience with vector databases and embedding pipelines (e.g., Pinecone, Weaviate, Qdrant, pgvector).”
The ad says“Strong Python skills and experience with data pipeline frameworks.”
The ad says“Experience with vector databases and embedding pipelines (e.g., Pinecone, Weaviate, Qdrant, pgvector).”
The ad says“Practical experience implementing RAG architectures or LLM-integrated data pipelines.”
The ad says“Familiarity with semantic web standards—JSON-LD, RDF, OWL, or SKOS.”
The ad says“Familiarity with semantic web standards—JSON-LD, RDF, OWL, or SKOS.”
The ad says“Experience with vector databases and embedding pipelines (e.g., Pinecone, Weaviate, Qdrant, pgvector).”
The ad says“Experience with vector databases and embedding pipelines (e.g., Pinecone, Weaviate, Qdrant, pgvector).”

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 comply.com

About the role

Comply is looking for a Senior AI Data Engineer to turn our semantic data models into the knowledge graphs, vector search, and LLM-powered pipelines that make Comply’s financial and regulatory data genuinely AI-ready. Comply is the world’s leading aggregator of financial and regulatory data for compliance, ingesting and enriching vast volumes of complex, high-variance data from hundreds of brokers and data providers across the US and beyond. The Comply Data Platform is a strategic initiative at the heart of that mission: a modern, cloud-native semantic layer built from the ground up—using JSON-LD as its semantic language—to power AI-driven insights, regulatory analytics, and next-generation data products. In this hands-on role at the intersection of knowledge representation, AI infrastructure, and data platform engineering, you’ll own the delivery of semantic layer components, work hand-in-hand with data engineers, architects, and our ontologist, and make sure AI-ready data products are reliable, performant, and actually adopted. You’ll join a new team being created to enable Comply’s AI ambitions.

You’ll love this role if you’re an engineer who turns semantic and ontological concepts into concrete, production-grade systems—and you’re energized by greenfield environments where the architecture is still being defined.

What you’ll do

Implement JSON-LD-based semantic models from our ontologist into production data systems, and build knowledge graph structures that reflect canonical domain models.

Develop and manage graph database schemas, queries, and ingestion pipelines, keeping semantic consistency between ontology definitions and downstream data products.

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 architectures that ground LLM outputs in Comply’s proprietary data, and evaluate and integrate LLM tooling suited to our use cases.

Build reliable, observable data pipelines that feed the semantic layer from upstream broker and regulatory sources, applying DataOps practices such as testing, monitoring, lineage tracking, and SLAs.

Work with Data and Backend Engineers to embed semantic models into APIs and data contracts, ensuring the semantic layer scales with data volume and platform growth.

Partner closely with the Ontologist so implemented models faithfully reflect domain intent, and support consuming application teams in adopting AI-ready data products.

What success looks like in your first 90 days

You’ve shipped your first semantic layer components into production, turning ontology models into working knowledge graph structures.

You’ve stood up or extended embedding and vector infrastructure that a consuming team is actively using for semantic search or RAG.

You’ve put DataOps practices in place so pipelines are observable and maintain semantic quality from ingestion through to consumption.

What you’ll bring

Must-have

Strong hands-on data engineering experience, 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).

Nice-to-have

Exposure to domain-driven design (DDD) and bounded contexts.

Experience working directly with ontologists or knowledge engineers.

Familiarity with data contracts and data product frameworks.

Experience with DataOps tooling, data reliability, or data observability platforms.

Background in financial services, RegTech, or compliance data.

To learn more about our values, mission and the wide-range of perks offered to employees at Comply, visit https://www.comply.com/careers/.

Comply is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, disability, sex, sexual orientation, gender identity, or national origin. Nothing in this job posting should be construed as an offer or guarantee of employment.

Applicants must be authorized to work for any employer in the United Kingdom. Currently, we are unable to sponsor or take over sponsorship of an employment Visa at this time.

Comply is aware of scammers posing as Comply employees and extending job offers via direct messaging, texts and social media platforms. These are fraudulent and should be treated as such. To learn more about this, please review our Statement of Fraudulent Job Offers.

Asks for

Values