Senior AI Data Engineer – Semantic Layer & Vector AI

Senior AI Data Engineer – Semantic Layer & Vector AI

Full-Time 60000 - 80000 £ / year (est.) Home office (partial)
COMPLY

At a Glance

  • Tasks: Implement AI-driven data solutions and collaborate on innovative projects in the financial sector.
  • Company: Join Comply, a leader in compliance SaaS for global financial services.
  • Benefits: Enjoy competitive salary, health perks, and opportunities for professional growth.
  • Other info: Be part of a dynamic team with excellent career advancement opportunities.
  • Why this job: Make a real impact by shaping AI infrastructure and enhancing compliance solutions.
  • Qualifications: Experience in data engineering, semantic models, and AI infrastructure is essential.

The predicted salary is between 60000 - 80000 £ 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 products
  • 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.

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.

Senior AI Data Engineer – Semantic Layer & Vector AI employer: COMPLY

Comply is an exceptional employer, offering a dynamic work environment where innovation meets compliance in the financial services sector. With a strong focus on employee growth and collaboration, Comply provides extensive training opportunities and a culture that values diversity and inclusion. Located in the heart of the UK, employees benefit from a vibrant city life while contributing to cutting-edge AI and data engineering projects that shape the future of regulatory compliance.

COMPLY

Contact Details:

COMPLY Recruitment Team

StudySmarter Expert Advice🤫

We think this is how you could land Senior AI Data Engineer – Semantic Layer & Vector AI

Tip Number 1

Network like a pro! Reach out to folks in the industry, especially those at Comply. A friendly chat can open doors and give you insights that a job description just can't.

Tip Number 2

Show off your skills! If you've got a portfolio or projects that highlight your experience with semantic layers or AI data infrastructure, make sure to share them during interviews. It’s all about demonstrating what you can bring to the table.

Tip Number 3

Prepare for technical questions! Brush up on your knowledge of graph databases and embedding pipelines. Being able to discuss these topics confidently will show that you're ready for the hands-on nature of the role.

Tip Number 4

Apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you’re genuinely interested in joining the Comply team.

We think you need these skills to ace Senior AI Data Engineer – Semantic Layer & Vector AI

Data Engineering
Semantic Layer Implementation
Knowledge Graphs
Graph Databases
Vector Databases
Embedding Pipelines
RAG Architectures

Some tips for your application 🫡

Show Off Your Skills:Make sure to highlight your hands-on experience with data engineering and AI infrastructure. We want to see how your skills align with the role, so don’t hold back on showcasing your expertise in knowledge graphs and vector databases!

Tailor Your Application:Take a moment to customise your application for this specific role. Use keywords from the job description to demonstrate that you understand what we’re looking for. This helps us see how you fit into our team at Comply.

Be Clear and Concise:When writing your application, keep it clear and to the point. We appreciate well-structured applications that are easy to read. Avoid jargon unless it’s relevant to the role – we want to know about your experience without getting lost in technical terms!

Apply Through Our Website:Don’t forget to apply through our website! It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, you’ll find all the info you need about our values and perks there!

How to prepare for a job interview at COMPLY

Know Your Semantic Layer

Make sure you understand the concept of a semantic layer and how it applies to AI data engineering. Brush up on JSON-LD, RDF, and OWL standards, as well as how they relate to knowledge graphs. Being able to discuss these topics confidently will show your expertise.

Showcase Your Hands-On Experience

Prepare to discuss specific projects where you've built or operated knowledge graphs or vector databases. Highlight your experience with tools like Neo4j or Pinecone, and be ready to explain the challenges you faced and how you overcame them.

Collaboration is Key

Since this role involves working closely with ontologists and application teams, think of examples that demonstrate your collaborative skills. Be prepared to talk about how you’ve partnered with others to implement data models or resolve integration challenges.

DataOps Practices Matter

Familiarise yourself with DataOps principles, especially around testing, monitoring, and lineage tracking. Be ready to discuss how you've applied these practices in previous roles to ensure data reliability and observability, as this will resonate well with the interviewers.