At a Glance
- Tasks: Implement AI-driven data solutions and build knowledge graphs for financial compliance.
- Company: Join Comply, a leader in compliance SaaS for global financial services.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Dynamic team environment with exciting career advancement opportunities.
- Why this job: Be at the forefront of AI innovation and make a real impact in compliance.
- Qualifications: Experience in data engineering, knowledge graphs, and AI infrastructure required.
The predicted salary is between 63000 - 77000 £ per year.
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.
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
- 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 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
- 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
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.
All qualified applicants will receive consideration for employment without regard to race, color, religion, disability, sex, sexual orientation, gender identity, or national origin. Currently, we are unable to sponsor or take over sponsorship of an employment Visa at this time.
Senior AI/Data Science Engineer. in London employer: ComplySci
At Comply, we pride ourselves on being a leading employer in the financial services sector, offering a dynamic work environment that fosters innovation and collaboration. Our commitment to employee growth is evident through our comprehensive training programs and opportunities to work on cutting-edge AI projects, all while enjoying a supportive culture that values diversity and inclusion. Located in a vibrant area, our team enjoys not only competitive benefits but also the chance to make a meaningful impact in compliance technology.
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We think this is how you could land Senior AI/Data Science Engineer. in London
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We think you need these skills to ace Senior AI/Data Science Engineer. in London
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