AI Engineering Lead — ML Platforms & Risk Solutions

Talent Octopusventures; City Of London; On-site; GBP 90,000 - 140,000; Full time

Job summary

LexisNexis Risk Solutions is seeking a Technical Leader to guide a multidisciplinary team delivering AI-enhanced risk products and internal tools at scale. You will blend hands-on engineering with people leadership to build secure, scalable ML services and platform capabilities.

You will drive architecture for deployment pipelines, APIs, and data/feature systems, while promoting best practices in DevOps, security, and observability across teams.

Qualifications

• 7+ years in backend, full‑stack, ML engineering, or distributed systems.
• 2+ years in technical leadership or senior mentoring roles.
• Hands-on experience deploying ML-powered services into production.
• Strong Python and Java in production services.
• Experience with Snowflake, Spark, Databricks or similar tech, CI/CD pipelines, and modern DevOps tooling.
• Solid SecOps understanding and secure system design.
• Proven initiative and ability to work independently.

Responsibilities

• Lead and grow a multidisciplinary engineering team (ML, backend, UI).
• Define technical direction for AI-enhanced services and internal tools.
• Guide architecture for model deployment pipelines and APIs.
• Ensure high quality delivery across code, tests, docs, and observability.
• Collaborate with Product, Architecture, and ML Research teams to prioritise work.
• Foster modern AI development practices including MLOps and automation.
• Set and enforce DevOps and SecOps standards across services.

Skills

Backend engineering

ML engineering

Full-stack development

Distributed systems

Python

Java

DevOps tooling

Tools

Snowflake

Spark

Databricks

CI/CD pipelines

DevOps tooling

Job description

LexisNexis Risk Solutions is seeking a Technical Leader to guide a multidisciplinary team delivering AI-enhanced risk products and internal tools at scale. You will blend hands-on engineering with people leadership to build secure, scalable ML services and platform capabilities.

You will drive architecture for deployment pipelines, APIs, and data/feature systems, while promoting best practices in DevOps, security, and observability across teams.