AI Data Engineer [T500-28265]

AI Data Engineer (Data & Analytics)Data and Analytics at COMPLY:COMPLY is the world’s leading aggregator of financial and regulatory data to support compliance. Our mission is to help financial institutions meet their regulatory obligations with confidence, clarity, and speed. We ingest, process, and enrich vast volumes of complex, high-variance data from hundreds of brokers, data providers and other sources of information across the US and beyond. The scale, diversity, and importance of this data creates unique technical challenges and opportunities for innovative engineers.The COMPLY Data Platform is a strategic initiative at the heart of this mission. We are building a modern, cloud- native semantic layer from the ground up — using JSON-LD as our semantic language — to power AI-driven insights, regulatory analytics, and next-generation data products.Our AI Data Engineers work hand-in-hand with the data engineers, architects, and ontologist to translate semantic models into production-grade knowledge graphs, embedding pipelines, and RAG architectures that make Comply’s data genuinely AI-ready.The Role:We are looking for an AI Data Engineer 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.Key Responsibilities:Semantic Layer ImplementationImplement JSON-LD-based semantic models designed by the ontologist into production data systemsBuild and maintain knowledge graph structures that reflect canonical domain modelsDevelop and manage graph database schemas, queries, and data ingestion pipelinesEnsure semantic consistency between ontology definitions and downstream data products AI & Vector InfrastructureDesign and implement embedding pipelines that represent Comply’s financial and regulatory data in vector spaceBuild and operate vector database infrastructure for semantic search and similarity retrievalImplement RAG (Retrieval-Augmented Generation) architectures that ground LLM outputs in Comply’s proprietary dataEvaluate and integrate LLM tooling and frameworks appropriate to Comply’s use cases Data Pipeline & Platform EngineeringBuild reliable, observable data pipelines that feed the semantic layer from upstream broker and regulatory data sourcesApply DataOps practices including testing, monitoring, lineage tracking, and SLAsWork with Data Engineers and Backend Engineers to embed semantic models into APIs and data contractsEnsure the semantic layer scales with data volume and platform growth Collaboration & EnablementPartner closely with the Ontologist to ensure implemented models faithfully reflect domain intentSupport consuming application teams in understanding and adopting AI-ready data productsContribute to resolving cross-domain data integration challengesEssential Criteria:Strong hands-on experience in data engineering, with a focus on semantic or AI data infrastructureExperience 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 pipelinesFamiliarity with semantic web standards — JSON-LD, RDF, OWL, or SKOSStrong Python skills and experience with data pipeline frameworksExperience with cloud-native data platforms (AWS, Azure, or GCP)Desirable Criteria:Exposure to domain-driven design (DDD) and bounded contextsExperience working directly with ontologists or knowledge engineersFamiliarity with data contracts and data product frameworksExperience with DataOps tooling, data reliability, or data observability platformsBackground in financial services, RegTech, or compliance dataWay of Working:Collaborative and pragmatic — focused on adoption and delivery, not theoretical completenessComfortable working across engineering and domain boundariesAble to translate semantic and ontological concepts into concrete engineering decisionsConfident navigating greenfield environments where architecture is still being definedImpact and Outcomes:A production-grade semantic layer that is consistent, scalable, and used in practice by consuming teamsEmbedding and vector infrastructure that enables Comply’s AI-powered data productsReliable, observable pipelines that maintain semantic quality from ingestion through to consumptionReduced time-to-value for new AI features through reusable semantic infrastructure