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:

• 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.
• 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.
• Strong understanding of responsible AI practices, including evaluation, traceability, secure data handling, model governance, human oversight, and risk management.
• Practical experience with LLM-based capabilities, including retrieval-augmented generation, semantic search, embeddings, prompt design, evaluation, guardrails, and observability.
• Experience with agentic workflows, tool orchestration, and multi-step AI processes.
• Strong AWS knowledge, including cloud-hosted applications, data services, security controls, logging, monitoring, and production support.
• Strong understanding of SDLC practices, including requirements analysis, design, implementation, automated testing, code review, secure coding, deployment, and production support.
• Ability to work with structured, semi-structured, and unstructured data sources.
• Ability to understand legacy systems, domain processes, data flows, and integration constraints.
• Practical experience using AI-assisted development tools such as GitHub Copilot, Codex, Claude, or similar tools to improve software delivery.
• Strong problem-solving skills, including identifying, researching, troubleshooting, and resolving complex technical, data, and integration issues.
• Strong communication and technical writing skills, including the ability to explain ML and engineering concepts clearly to technical and non-technical stakeholders.

Desirable experience:

• Docker, Kubernetes/K8s, AWS EKS or ECS, Terraform, or similar cloud deployment technologies.
• C#/.NET and SQL Server, particularly for integration with enterprise or legacy systems.
• Event-driven architecture, messaging, queues, asynchronous processing, retries, idempotency, and failure handling.
• Legal content systems, LegalTech, publishing platforms, case law, citation systems, legal research workflows, XML/XSLT, structured content processing, search, ranking, indexing pipelines, or content enrichment.

Why Join Us?

Join our team and contribute to a culture of innovation, collaboration, and excellence. If you are ready to advance your career and make a significant impact, we encourage you to apply.

Work in a way that works for you

We promote a healthy work/life balance across the organization. We offer an appealing working prospect for our people. With numerous wellbeing initiatives, shared parental leave, study assistance and sabbaticals, we will help you meet your immediate responsibilities and your long-term goals.

Benefits:

• Working flexible hours - flexing the times when you work in the day to help you fit everything in and work when you are the most productive.
• Generous holiday allowance with the option to buy additional days.
• Health screening, eye care vouchers, and private medical benefits.
• Wellbeing programmes.
• Life assurance.
• Access to a competitive contributory pension scheme.
• Save As You Earn share option scheme.
• Maternity, paternity, and shared parental leave.
• Employee Assistance Programme.
• Access to employee resource groups with dedicated time to volunteer.
• Access to extensive learning and development resources.
• Access to employee discounts scheme via Perks at Work.

About the business:

At LexisNexis GTO, you'll have the opportunity to work on business-critical products while leveraging some of the latest AI and software engineering technologies. We actively invest in developer productivity tools such as GitHub Copilot, ChatGPT, and Codex, and provide regular training, workshops, office hours, and communities of practice to help engineers continually develop their skills. Our engineers are encouraged to innovate and experiment with emerging technologies, including AI-powered solutions, agentic workflows, modern cloud platforms, and intelligent content systems that directly impact our products and customers.

GTO fosters a strong culture of learning and collaboration through initiatives such as the Development Guild, Search & AI Guild, technical communities, engineering showcases, and regular knowledge-sharing events where teams learn from one another and share best practices. As part of a global technology organisation, you'll gain exposure to colleagues, products, and initiatives across multiple regions, supported by regular GTO and AI & Platforms town halls that provide visibility into company strategy, innovation, and senior leadership priorities.

Beyond technical growth, LexisNexis and RELX provide a wealth of professional development opportunities through structured learning programmes, leadership development, wellbeing initiatives, and extensive learning resources to help employees grow throughout their careers. We know your well-being and happiness are key to a long and successful career.

We are committed to providing a fair and accessible hiring process. If you have a disability or other need that requires accommodation or adjustment, please let us know by completing our Applicant Request Support Form or please contact 1-855-833-5120.

We are an equal opportunity employer: qualified applicants are considered for and treated during employment without regard to race, color, creed, religion, sex, national origin, citizenship status, disability status, protected veteran status, age, marital status.