ML Engineer – Real-Time Fraud & Identity Analytics

Talent Octopusventures; City Of London; On-site; GBP 90,000 - 110,000; Full time

Benefits offered by this job

Private medical benefits

Pension scheme

Life assurance

Travel season ticket loan

Dental Insurance

Learning resources

Job summary

LexisNexis Risk Solutions is seeking a software engineer to build and deploy ML-powered services, tools, and full-stack applications supporting fraud and identity analytics. You will work across backend services, model-serving pipelines, and user interfaces.

You will develop ML inference APIs, data pipelines, and production system integrations with automation, monitoring, and secure engineering at the core, collaborating with data scientists, architects, and QA.

Qualifications

• 4+ years software engineering (backend, full-stack, or ML).
• Strong Python and Java proficiency.
• Experience with Snowflake or similar data platforms.
• Familiarity with ML model serving and feature engineering.
• Demonstrated ownership and ability to work independently.
• Working knowledge of DevOps and secure engineering practices.

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 model serving

Feature engineering

DevOps

Secure engineering

Ownership

Independent execution

Tools

Snowflake

Job description

LexisNexis Risk Solutions is seeking a software engineer to build and deploy ML-powered services, tools, and full-stack applications supporting fraud and identity analytics. You will work across backend services, model-serving pipelines, and user interfaces.

You will develop ML inference APIs, data pipelines, and production system integrations with automation, monitoring, and secure engineering at the core, collaborating with data scientists, architects, and QA.