AI Engineer in London

Norton Rose Fulbright is a global law firm with more than 3,000 lawyers advising clients across locations in the United States, Europe, Canada, Latin America, Asia, Australia, Africa and the Middle East. We provide a full scope of legal services to the world’s preeminent corporations and financial institutions. Our vision is to be a world class business, profitable, ambitious, cooperative and considerate, supporting our clients and people through our global business principles of Quality, Unity and Integrity.

We are building a new R&D capability focused on developing data-driven and AI-enabled solutions for legal services and the wider business of law. The AI Engineer will build end-to-end AI capabilities for R&D products: agentic workflows, retrieval and context, agent and evaluation harnesses, and the user-facing features around them. Working with the Head of R&D, you will translate agreed product and technical designs into secure, observable and maintainable products. This is a hands-on full-stack AI engineering role, with applied AI product delivery at its core.

Key Responsibilities

• Build complete AI-enabled product features across user interface, backend, AI orchestration, retrieval and approved enterprise integrations.
• Design and build agentic AI workflows using structured outputs, tool calling, workflow orchestration, document processing, human-review steps and appropriate controls.
• Build reusable agent harnesses: practical runtime patterns around tools, state, context, approvals, traces and test fixtures that make agents consistent, inspectable and safe to use.
• Build grounded retrieval and context patterns using approved data products, search indexes, document repositories and permission-aware sources; provide evidence and citations where appropriate.
• Design and operate evaluation harnesses for AI workflows, including curated test sets, representative scenarios, automated and human grading, regression tests, trace review and user-feedback loops.
• Manage prompts, model configuration, tool schemas and routing as tested product assets, including fallbacks and cost/latency trade-offs.
• Implement AI observability through traces, logs, metrics and evaluation results, so quality, reliability, failure modes, latency and cost are visible and can be improved.
• Build clear APIs and integrate AI products with approved data, document, search and business systems.
• Build usable interfaces that help users understand AI output, inspect supporting evidence, provide feedback and complete review or approval steps.
• Write maintainable Python and TypeScript code; use Docker, Git, automated testing and CI/CD to make development, testing and deployment repeatable.
• Apply agreed security, authentication, data-handling, deployment and release patterns, working with the relevant firm technology teams where needed.

Essential Skills and Experience

• At least three years’ professional experience delivering production AI products, with demonstrable recent experience building AI-enabled products or workflows in a SaaS environment.
• Strong Python, plus practical TypeScript or JavaScript.
• Able to build APIs and services using FastAPI, Flask or comparable technologies, and usable interfaces using React, Next.js or equivalent.
• Demonstrable experience building AI-enabled products or workflows using LLM APIs, structured outputs, retrieval and embeddings, tool calling, agents or workflow orchestration.
• Experience evaluating AI workflows using representative test sets, scenario design, model or prompt experiments, trace review, user feedback, and automated or human grading.
• Experience taking AI features from prototype into controlled production, including testing, monitoring, troubleshooting and ongoing improvement.
• Experience with AI observability, tracing and evaluation tooling.
• Strong understanding of grounded retrieval, context quality, evidence/citations and the ways in which AI systems fail.
• Comfort with Docker, Git, automated testing and CI/CD.
• Strong debugging and systems-thinking skills, including the ability to diagnose failures across models, prompts, tools, retrieval and integrations.

Diversity, Equity and Inclusion

To attract the best people, we strive to create a diverse and inclusive environment where everyone can bring their whole selves to work, have a sense of belonging, and realize their full career potential. Our new enabled work model allows our people to have more flexibility in the way they choose to work from both the office and a remote location, while continuing to deliver the highest standards of service.