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# Machine Learning Engineer/AI Engineer

- **Company:** Businesslexisnexis Risk Solutions
- **Location:** London, UK
- **Contract:** Permanent contract
- **Skills:** Testing (Software), Java (Programming Language), Application Programming Interfaces (APIs), Artificial Intelligence, Component-Based Software Engineering, Automation of Tests, Code Review, Computer Programming, Continuous Integration, Data Stores, Relational Databases, Decision Support Systems, Distributed Systems, Python (Programming Language), Machine Learning, Modular Design, Object-Oriented Software Development, Secure Coding, Software Engineering, Lexis, Feature Engineering, Snowflake, Backend, Data Management, Api Design, Software Version Control
- **Published:** September 21, 2026
- **Apply:** https://www.apply4u.co.uk/jobs/machine-learning-engineerai-engineer/47822301

## About the Role

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.RequirementsProfessional 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 statementLearn more about the LexisNexis Risk team and how we work here We know your well-being and happiness are key to a long and successful career. We are delighted to offer country specific benefits. Click here to access benefits specific to your location.We are committed to providing a fair and accessible hiring process. If you have a disability or other need that requires accommodation or adjustment, please let us know by completing our Applicant Request Support Form or please contact 1-855-833-#####.Criminals may pose as recruiters asking for money or personal information. We never request money or banking details from
job applicants. Learn more about spotting and avoiding scams here.Please read our Candidate Privacy Policy.We are an equal opportunity employer: qualified applicants are considered for and treated during employment without regard to race, color, creed, religion, sex, national origin, citizenship status, disability status, protected veteran status, age, marital status, sexual orientation, gender identity, genetic information, or any other characteristic protected by law.USA Job Seekers:EEO Know Your Rights.SummaryLocation: UK - London (London Wall)Type: Full time

## Description

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.ResponsibilitiesDesign, 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


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