Lead Knowledge Graph Architect for Risk Analytics

Intelix.AI; Greater London; Hybrid; GBP 120,000 - 200,000; Full time

Job summary

Intelix.AI is seeking a Head of Knowledge Graph Engineering in London (hybrid) to lead the architecture of a graph-based analytics platform for credit risk. You will own semantic models, data mappings, provenance and temporal rules, coordinating with data science and engineering teams to deliver a validated first implementation.

The role requires senior expertise in knowledge graphs, graph databases, and connected data applications, with hands-on tool experience in Python, SQL, Cypher, SPARQL

Qualifications

• Knowledge graph engineering, semantic modelling and graph query design for risk data
• Experience with graph-grounded AI and connected data applications for analytics
• Ownership of a knowledge graph implementation from model to working product

Responsibilities

• Graph build: initial graph models, data mappings, queries and working implementations
• Languages and tools: Python, SQL, Cypher, SPARQL, RDF, property graphs
• Semantic model: analytical domains, subjects, capabilities, methods, inputs, outputs
• Relationship modelling: identity, time, provenance, validation; meaning, timing, source and limits of each relationship
• Risk pathways: country and industry developments linked to exposures and credit outcomes
• Graph and AI: connected retrieval, grounded answers, traceable sources
• Model testing: graph relationships, features and algorithms tested with data scientists
• Content enrichment: tagging with research and content teams linking documents, topics, entities, events and analytical capabilities
• Delivery: ingestion, validation and interfaces with engineering and platform teams

Skills

Knowledge graph engineering

Semantic modelling

Graph query design

Risk data

Graph-grounded AI

Tools

Python

SQL

Cypher

SPARQL

RDF

Property graphs

Job description

Intelix.AI is seeking a Head of Knowledge Graph Engineering in London (hybrid) to lead the architecture of a graph-based analytics platform for credit risk. You will own semantic models, data mappings, provenance and temporal rules, coordinating with data science and engineering teams to deliver a validated first implementation.

The role requires senior expertise in knowledge graphs, graph databases, and connected data applications, with hands-on tool experience in Python, SQL, Cypher, SPARQL