Full-Stack ML Engineer - Fraud & Identity Analytics

LexisNexis Risk Solutions; Greater London; On-site; GBP 80,000 - 120,000; Full time

Benefits offered by this job

Generous holiday allowance

Private medical benefits

Life assurance

Contributory pension scheme

Save As You Earn share option scheme

Travel Season ticket loan

Electric Vehicle Scheme

Optional Dental Insurance

Employee Assistance Programme

Learning and development resources

Perks at Work discounts

Job summary

LexisNexis Risk Solutions is seeking an experienced engineer to build and deploy ML-powered services, tools, and full-stack applications that support fraud and identity analytics. You will work across backend services, model-serving pipelines, and user interfaces to deliver robust, scalable solutions.

You will develop ML inference APIs, data pipelines, and production-grade microservices, while integrating ML models into real-time systems and ensuring secure, auditable operations.

Qualifications

• 4+ years software engineering (backend, full-stack, or ML).
• Strong Python and Java.
• Snowflake or similar data‑platform experience.
• Familiarity with ML model serving and feature engineering.
• Strong ownership and independent execution.
• Working knowledge of DevOps and secure engineering.

Responsibilities

• Develop ML inference APIs, microservices, and data/feature pipelines.
• Build full-stack tools to support model evaluation and transparency.
• Integrate ML models into real-time production systems.
• Implement automated training, monitoring, and evaluation workflows.
• Use and contribute to AI-assisted development tools.
• Own DevOps and security standards for assigned services.
• Collaborate with data scientists, architects, and QA.

Skills

Python

Java

ML/AI

DevOps concepts

Problem solving

Tools

Snowflake

Snowpark

dbt

Vector databases

LLMs

Job description

LexisNexis Risk Solutions is seeking an experienced engineer to build and deploy ML-powered services, tools, and full-stack applications that support fraud and identity analytics. You will work across backend services, model-serving pipelines, and user interfaces to deliver robust, scalable solutions.

You will develop ML inference APIs, data pipelines, and production-grade microservices, while integrating ML models into real-time systems and ensuring secure, auditable operations.