Full‑Stack Machine Learning Engineer
London, United Kingdom, United Kingdom • Posted June 03, 2026
Job Type:
Full-time
Location:
London, United Kingdom
Posted:
June 03, 2026
Category:
other-general
Application Deadline:
June 08, 2026
Role Description
About the Business
LexisNexis Risk Solutions provides customers with solutions and decision tools that combine public and industry specific content with advanced technology and analytics to assist them in evaluating and predicting risk and enhancing operational efficiency. We use the power of data and advanced analytics to help our customers make better, timelier decisions. By bringing clarity to information, we ultimately help make communities safer, commerce more transparent, business decisions easier and processes more efficient. You can learn more about LexisNexis Risk solutions at the link below, https://risk.lexisnexis.com/
About the role:Build and deploy ML‑powered services, tools, and full‑stack applications supporting fraud and identity analytics. Work across backend services, model‑serving pipelines, and user interfaces.
Key Responsibilities
+ Develop ML inference APIs, microservices, and data/feature pipelines.
+ Build full‑stack ...
LexisNexis Risk Solutions provides customers with solutions and decision tools that combine public and industry specific content with advanced technology and analytics to assist them in evaluating and predicting risk and enhancing operational efficiency. We use the power of data and advanced analytics to help our customers make better, timelier decisions. By bringing clarity to information, we ultimately help make communities safer, commerce more transparent, business decisions easier and processes more efficient. You can learn more about LexisNexis Risk solutions at the link below, https://risk.lexisnexis.com/
About the role:Build and deploy ML‑powered services, tools, and full‑stack applications supporting fraud and identity analytics. Work across backend services, model‑serving pipelines, and user interfaces.
Key Responsibilities
+ Develop ML inference APIs, microservices, and data/feature pipelines.
+ Build full‑stack ...
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