Machine Learning Engineering Lead

Salary: £45,000 - 85,000 per year

Requirements:

• Significant hands-on experience in machine learning engineering, software engineering, data engineering, or a related technical discipline.
• Experience designing, building, deploying, and operating production ML, AI, LLM, or data-driven systems.
• 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 is 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.
• 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.
• Strong understanding of responsible AI practices, including evaluation, traceability, secure data handling, model governance, human oversight, and risk management.
• 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 with Docker, Kubernetes/K8s, AWS EKS or ECS, Terraform, or similar cloud deployment technologies.
• Desirable: Working knowledge of C#/.NET and SQL Server, particularly for integration with enterprise or legacy systems.
• Desirable: Experience with event-driven architecture, messaging, queues, asynchronous processing, retries, idempotency, and failure handling.
• Desirable: Experience with 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.

Responsibilities:

• Serve as the initial point of escalation for AI/ML engineering issues within our area of responsibility.
• Work 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 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 reliable, traceable, and governed agentic workflows, tool orchestration, and multi-step AI processes.
• 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.
• 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 on AI/ML engineering, MLOps, SDLC practices, and responsible use of AI-assisted development tools.
• Keep abreast of 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.
• Perform other duties as assigned.

Technologies:

• AI
• AWS
• C#
• CI/CD
• Cloud
• Copilot
• Docker
• GitHub
• Support
• Kubernetes
• LLM
• Machine Learning
• MLOps
• Python
• RAG
• SQL
• Security
• Terraform
• Web
• XML
• ASP.NET
• ChatGPT

More:

We are hiring a Machine Learning Engineering Lead to provide subject matter expertise for production AI/ML, LLM/RAG, and agentic workflow capabilities for legal content products. The role combines hands-on coding on complex systems with technical direction in architecture, MLOps, responsible AI, legacy system integration, AWS-based delivery, and AI-assisted development; it has no direct reports. Our software engineering team develops and supports business-critical platforms for creating, managing, publishing, and analysing legal and regulatory content. We work across modern web applications, cloud services, content migration programmes, publishing platforms, reporting solutions, and operational tooling, partnering with editorial, product, and technology stakeholders and engineering teams across regions. At LexisNexis GTO, we invest in developer productivity tools and support ongoing learning through training, workshops, communities of practice, technical guilds, showcases, and knowledge-sharing events. We offer flexible working, wellbeing initiatives, shared parental leave, study assistance, sabbaticals, generous holiday allowance with the option to buy additional days, health screening, eye care vouchers, private medical benefits, life assurance, a contributory pension scheme, a share option scheme, an Employee Assistance Programme, employee resource groups, learning and development resources, and employee discounts. The role is full-time and based in Farringdon. We are an equal opportunity employer and are committed to a fair and accessible hiring process.

last updated 40 week of 2026