ML Engineering Lead — Flexible Hours, AWS & MLOps

LexisNexis Risk Solutions; United Kingdom; Remote; GBP 110,000 - 150,000; Full time

Benefits offered by this job

Generous holiday allowance

Private medical benefits

Learning and development

Job summary

LexisNexis GTO is seeking a Machine Learning Engineering Lead to design and deploy scalable ML systems for legal content products. You will act as SME for ML/AI, guide architecture, MLOps, and responsible AI practices while influencing engineering teams across regions.

You will mentor engineers, collaborate with software/data engineers and stakeholders, and ensure production AI/ML capabilities meet reliability, governance, and business value targets.

Qualifications

• Significant hands-on experience in ML engineering or related field.
• Experience deploying ML/AI in production with AWS.
• Strong Python development skills for ML pipelines.
• Experience with LLM, RAG, and agentic workflows.

Responsibilities

• Serve as the initial point of escalation for AI/ML engineering issues within the area of responsibility.
• Interface with software engineers, data engineers, product stakeholders, domain experts, platform teams, and other technical personnel to finalise requirements and clarify integration needs.
• Write and review portions of detailed specifications for the development of complex AI/ML, LLM, RAG, and agentic workflow components.
• Design, build, integrate, deploy, and operate production AI/ML and LLM-based services for legal research, analytics, and content use cases.

Skills

Python

AWS

ML engineering

MLOps

LLM/RAG

Docker/Kubernetes

Team mentoring

GitHub Copilot

Codex

Tools

Terraform

Job description

LexisNexis GTO is seeking a Machine Learning Engineering Lead to design and deploy scalable ML systems for legal content products. You will act as SME for ML/AI, guide architecture, MLOps, and responsible AI practices while influencing engineering teams across regions.

You will mentor engineers, collaborate with software/data engineers and stakeholders, and ensure production AI/ML capabilities meet reliability, governance, and business value targets.