Software Engineering Lead / Applied AI Engineering

What if you could lead a team shaping the next generation of AI-powered risk solutions that help businesses make faster, smarter, and more secure decisions?

Are you excited by the opportunity to combine technical leadership, machine learning, and platform engineering to deliver real-world impact at scale?

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 technical leader, you will guide a multidisciplinary engineering team responsible for delivering AI-enhanced products, internal tools, and platform capabilities. You will combine hands-on technical expertise with people leadership, helping the team build scalable, secure, and reliable machine learning services while driving innovation and operational excellence.

Responsibilities

• Lead and grow a team of full-stack ML engineers, QA engineers, and a UI developer.
• Define technical direction for AI-enhanced services, internal tools, and platform components.
• Drive architecture for model deployment pipelines, inference APIs, and data and feature systems.
• Ensure high-quality delivery across code quality, testing, documentation, and observability.
• Partner with Product, Architecture, and ML Research teams to prioritise and scope work.
• Foster a culture of modern AI development practices, including LLM tooling, MLOps, and automation.
• Set and enforce DevOps and SecOps standards across the team's services and pipelines.
• Coordinate cross-team dependencies and contribute to roadmap planning.

Requirements

• 7+ years in backend, full-stack, ML engineering, or distributed systems.
• 2+ years in technical leadership, team leadership, or senior mentoring roles.
• Hands-on experience deploying ML-powered services into production.
• Strong Python and Java, both of which are in active use across the team's production services.
• Experience with Snowflake, Spark, Databricks or similar technologies, CI/CD pipelines, and modern DevOps tooling.
• Solid understanding of SecOps practices and security-conscious system design.
• Demonstrable track record of taking initiative and driving work independently.
• Broad full-stack curiosity, with the ability to contribute outside a primary discipline when needed.

Risk benefit statement

Learn more about the LexisNexis Risk team and how we work here







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