AI Data Scientist

Gazelle Global; Greater London; Híbrido; GBP 70.000 - 105.000; Jornada completa

Descripción de la vacante

Gazelle Global seeks a Knowledge Engineer to build and operate data pipelines that populate the CDS knowledge graph at scale. You will transform structured and unstructured source data into governed knowledge assets, aligned with the Knowledge Modeller's schema to support retrieval and agent-based AI capabilities.

You will develop Python-based data engineering and ETL processes, define chunking and vector embedding strategies for RAG, and ensure data quality, freshness and lineage across the

Formación

• Hands-on experience building and populating knowledge graphs programmatically.
• Strong Python data engineering experience, including ingestion and ETL at scale.
• Experience with structured and unstructured data sources.
• Exposure to regulated or government environments.

Responsabilidades

• Build and operate data pipelines that populate CDS knowledge graph at scale.
• Transform data into governed, high-quality knowledge assets per schema.
• Define chunking and vector embedding strategies for retrieval.
• Collaborate with AI engineers to support RAG and agent-based use cases.
• Maintain data quality, freshness and lineage of the knowledge graph.

Conocimientos

Neo4j/Cypher

Python

ETL pipelines

Knowledge graphs

RAG

LangGraph

Herramientas

Neo4j/Cypher

RDF/SPARQL

OpenSearch

AWS Bedrock

LangGraph

Descripción del empleo

We are building an enterprise-grade AI platform to enable secure, scalable and production-ready use of Generative AI and ML across the Customs Declaration Service.

The Knowledge Engineer will build and operate the data pipelines that populate and maintain the CDS knowledge graph at scale. You will transform structured and unstructured source data into a governed, high-quality knowledge asset, working from the Knowledge Modeller's schema and platform retrieval design to support RAG and agent-based AI capabilities.

Key Responsibilities

• Build and maintain ingestion pipelines that programmatically populate knowledge graphs from structured and unstructured data sources.
• Develop graph population workflows covering entity and relationship extraction, loading and validation against defined schemas.
• Work with graph databases and query technologies such as Neo4j/Cypher or RDF/SPARQL.
• Develop Python-based data engineering and ETL processes for high-volume ingestion.
• Define and maintain effective chunking and vector embedding strategies for content used in knowledge retrieval.
• Monitor and maintain knowledge graph data quality, freshness and lineage.
• Implement reconciliation processes to identify and address ingestion gaps, including missed webhook events, without placing additional load on source systems.
• Collaborate with AI Engineers to ensure the knowledge graph and retrieval layer effectively support RAG and agent-based use cases.
• Contribute to the evaluation of graph and retrieval quality, with a focus on data completeness and coverage.
• Hands-on experience building and populating knowledge graphs programmatically, rather than through UI-based curation.
• Strong experience with Neo4j/Cypher, RDF/SPARQL or equivalent graph technologies.
• Strong Python data engineering experience, including ingestion and ETL at scale.
• Practical experience with vector embeddings and chunking strategies for RAG or knowledge retrieval.
• Experience working with structured and unstructured data sources.
• Experience with LangGraph or equivalent AI orchestration frameworks.
• Exposure to AWS-native AI services such as Amazon Bedrock and OpenSearch.
• Experience working within regulated or government environments.