Senior AI Data Engineer

Ai-ready
London, UK
21 days ago
Apply on www.apply4u.co.uk
Prepare application

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Expert
Working hours
Regular working hours

Tech stack

Application Programming Interfaces (APIs) Artificial Intelligence Amazon Web Services Microsoft Azure Encodings Information Engineering Data Infrastructure Data Integration Data Integrity Graph Database JSON Python (Programming Language)
+9 more
Neo4j Cloud Services DataOps Semantic Web Large Language Models Backend Data Lineage Domain Driven Design Data Pipelines

Requirements

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. 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. 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 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. #J-18808-Ljbffr

About the company

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. 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

Apply for this position

This job is hosted externally. Click below to view the full posting and apply.

Apply on www.apply4u.co.uk
Prepare application

Good distractions

Talks and stories from around this role — technically off-topic, practically not.

3:03 min

Career evolution in data engineering and AI platforms

Maria Apazoglou · Coffee With Developers

3:47 min

Exploring JSON, CBOR, and JOSE for data serialization

Aaron Russell · LIVE

2:24 min

Comparing Neo4j and GraphQL conceptual models

William Lyon · LIVE

2:32 min

Refactoring bulk frontend operations into scalable backend methods

Noam Honig · LIVE

1:59 min

Evolving roles in AI driven software teams

Ignacio Riesgo Ignacio Riesgo +1 · World Congress 2024

2:03 min

Distinguishing type definition constructs from data validation routines

Clemens Vasters Clemens Vasters · World Congress 2025

Videos

See all

Related articles

See all