Senior ML Engineering Lead — AI Platforms

LexisNexis Risk Solutions; Farringdon; On-site; GBP 90,000 - 150,000; Full time

Benefits offered by this job

Generous holiday

Private medical benefits

Wellbeing programs

Life assurance

Pension scheme

SAYE scheme

Parental leave

Employee Assistance Programme

Learning resources

Perks at Work discounts

Job summary

LexisNexis GTO is seeking a Machine Learning Engineering Lead to design, deploy and operate production AI/ML services for legal research and analytics. The role emphasizes architecture direction, MLOps, responsible AI, and AWS-based delivery, with no direct reports.

You will mentor engineers, drive reusable ML patterns, and ensure high quality, scalable systems while aligning with governance and security standards across regions.

Qualifications

• Significant hands-on experience in machine learning engineering, software engineering, data engineering, or a related technical discipline.
• Experience designing, building, deploying, and operating ML, AI, LLM, or data-driven systems in production.
• Experience integrating AI/ML services with enterprise systems, APIs, databases, data platforms, legacy applications, or internal services.
• Experience working with cross-functional teams to understand business processes, data flows, content repositories, integration points, and operational constraints.
• Experience working with AWS or cloud-hosted production environments.
• Equivalent technical experience or education considered.

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.
• Implement RAG, semantic search, embeddings-based retrieval, ranking, summarisation, classification, content enrichment, and citation-aware AI capabilities where appropriate.
• Design and implement agentic workflows, tool orchestration, and multi-step AI processes that are reliable, traceable, and governed.
• Integrate AI/ML capabilities with enterprise systems, APIs, databases, data platforms, content repositories, legacy applications, internal services, and AWS-hosted services.
• Establish evaluation and quality controls for accuracy, groundedness, citation quality, hallucination risk, agent task success, latency, cost, reliability, and business value.
• Successfully implement development processes, coding best practices, code reviews, MLOps practices, and responsible AI controls.
• Apply AI-assisted development tools to reduce software development cycle time and support code explanation, test generation, refactoring, debugging, documentation, code review, migration planning, and legacy system analysis.
• Resolve complex technical issues related to AI/ML services, data flows, system integration, model behaviour, production support, and operational reliability.
• Mentor and/or train engineers as directed by department management, ensuring they are knowledgeable in critical aspects of AI/ML engineering, MLOps, SDLC practices, and responsible use of AI-assisted development tools.
• Keep abreast of relevant technology developments in machine learning engineering, LLMs, agentic workflows, AWS cloud services, responsible AI, and software engineering practices.
• Ensure AI/ML solutions align with enterprise data governance, security, privacy, responsible AI, and operational standards.
• All other duties as assigned.

Skills

ML engineering

Software engineering

Data engineering

Production systems

Enterprise integration

AWS cloud

Cross-functional teamwork

Equivalent experience

Tools

Docker

Kubernetes

AWS EKS

Terraform

SQL Server

Job description

LexisNexis GTO is seeking a Machine Learning Engineering Lead to design, deploy and operate production AI/ML services for legal research and analytics. The role emphasizes architecture direction, MLOps, responsible AI, and AWS-based delivery, with no direct reports.

You will mentor engineers, drive reusable ML patterns, and ensure high quality, scalable systems while aligning with governance and security standards across regions.