Full‑Stack Machine Learning Engineer

Are you excited to build and deploy ML-powered services, tools, and full-stack applications that support fraud and identity analytics?

Do you enjoy working across backend services, model-serving pipelines, and user interfaces to deliver solutions that make a real-world impact?

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 athttps://risk.lexisnexis.com/

About the Role

As a Software Engineer, you will build and deploy machine learning-powered services, tools, and full-stack applications that support fraud and identity analytics. You’ll work across backend systems, model-serving infrastructure, and user-facing applications, collaborating with cross-functional teams to deliver scalable, secure, and reliable solutions in production environments.

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.

Requirements

• 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.
• LLMs, embeddings, or vector databases.
• Behavioural, graph, or anomaly detection models.
• dbt, Snowpark, or Snowflake ML.

Risk benefit statement

Learn more about the LexisNexis Risk team and how we workhere

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