Senior AI Data Engineer
Indexed description
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
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.
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
- 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
- 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
- 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 DataOps tooling, data reliability, or data observability platforms is desirable.
- Background in financial services, RegTech, or compliance data is a plus.
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.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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