LexisNexis Risk Solutions
Machine Learning Engineer/AI Engineer
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Are you passionate about building scalable software that helps organisations detect fraud, verify identity, and make better decisions using advanced analytics?
Do you enjoy collaborating across engineering, data science, and product teams to turn intelligent solutions into reliable products that deliver real-world customer value?
About the Business
LexisNexis Risk Solutions is the essential partner in the assessment of risk. Within our Business Services vertical, we offer a multitude of solutions focused on helping businesses of all sizes drive higher revenue growth, maximize operational efficiencies, and improve customer experience. Our solutions help our customers solve difficult problems in the areas of Anti-Money Laundering/Counter Terrorist Financing, Identity Authentication & Verification, Fraud and Credit Risk mitigation, and Customer Data Management. You can learn more about LexisNexis Risk at https://risk.lexisnexis.com/
About the Role
You will join an engineering team building software for fraud and identity analytics. In this role, you will design, build, test, and operate scalable software products, working closely with data scientists, engineers, architects, product managers, and quality engineers. You will help bring machine learning and analytical capabilities into production systems, delivering secure, reliable, and maintainable solutions that create measurable customer value.
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Responsibilities
- Design, build, test, and maintain production-grade backend services and APIs using Python and Java.
- Integrate machine learning models and analytical components into real-time and batch software workflows.
- Develop reusable application components for feature calculation, inference, decision support, and model output interpretation.
- Build internal and customer-facing tools that help users explore, evaluate, and understand analytical outcomes.
- Apply sound software engineering practices, including modular design, code review, automated testing, documentation, and continuous improvement.
- Improve system performance, reliability, security, observability, and maintainability across the software lifecycle.
- Work with data scientists to translate prototypes and research outputs into robust, well-defined product capabilities.
- Participate in delivery and operational ownership for the services you build, including deployment, incident analysis, and remediation.
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Requirements
- Professional software engineering experience with a strong record of delivering production systems.
- Strong programming skills in Python and Java, including object-oriented design, clean interfaces, and maintainable application structure.
- Experience designing and developing APIs, backend services, distributed systems, or data-intensive applications.
- Solid understanding of software testing, version control, code review, CI/CD, secure development, and production support.
- Practical experience integrating machine learning models, statistical algorithms, or advanced analytics into software products.
- Ability to work with data stores and data platforms such as Snowflake, relational databases, or comparable technologies.
- Understanding of common machine learning concepts, feature engineering, inference, evaluation, and the limitations of analytical systems.
- Strong ownership, problem-solving, and communication skills, with the ability to execute independently and collaborate across disciplines.
Risk benefit statement
Learn more about the LexisNexis Risk team and how we work here.
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