Machine Learning Engineering Lead

relx; Farringdon; On-site; GBP 90,000 - 140,000; Full time

Job summary

Relx is seeking a Machine Learning Engineering Lead to drive design and deployment of AI/ML solutions that deliver business value across legal content platforms. You will guide architecture, MLOps practices, and enforce responsible AI while collaborating with cross-functional teams.

The role focuses on production systems, LLM/RAG, and agentic workflows within AWS-based services, with no direct reports but mentorship to engineers and a strong emphasis on reliability, governance, and measurable

Qualifications

• Significant hands-on experience in machine learning engineering and software engineering.
• Experience designing, building, deploying, and operating ML/AI systems in production.
• Experience integrating AI/ML services with enterprise systems and APIs.

Responsibilities

• Serve as the initial point of escalation for AI/ML engineering issues.
• Collaborate with engineers, product teams, and domain experts to finalize requirements.
• Design, build, and operate production AI/ML and LLM-based services for legal content products.
• Implement RAG, semantic search, embeddings retrieval, and content enrichment where appropriate.
• Mentor engineers and promote responsible AI practices.

Skills

Python development

Software engineering

Data engineering

AWS

Tools

CI/CD pipelines

APIs

Job description

Machine Learning Engineering Lead

Are you passionate about designing and deploying intelligent machine learning solutions that drive business impact?

Do you enjoy leading teams, building scalable ML systems, and turning complex data into innovative products and services?

About the team:

We are a software engineering team responsible for developing and supporting business-critical platforms used to create, manage, publish, and analyse legal and regulatory content. Our work spans modern web applications, cloud services, content migration programmes, publishing platforms, reporting solutions, and operational tooling. We partner with editorial, product, and technology stakeholders to deliver high-quality solutions that drive business value. In addition to supporting the UK business, we work closely with engineering teams across multiple regions to share expertise, promote reuse, and deliver scalable solutions that benefit the wider organisation.

About the role:

This position serves as a subject matter expert for Machine Learning Engineering, supporting production AI/ML, LLM/RAG, and agentic workflow capabilities for legal content products. In addition to writing code on complex systems, this position provides technical direction on architecture, MLOps, responsible AI, legacy system integration, AWS-based delivery, and AI-assisted development practices. The position does not have direct reports.

Key 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.

Requirements:

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.

Technical Skills:

Strong Python development skills for machine learning engineering, data processing, automation, service development, and production AI/ML workflows.

Strong software engineering background, including system design, APIs, distributed systems, automated testing, code review, maintainability, reliability, and production support.

Strong understanding of ML engineering and MLOps practices, including model lifecycle management, CI/CD, testing, monitoring, release management, observability, and operational support.

Pract