AI Engineer

Norton Rose Fulbright; Greater London; On-site; GBP 90,000 - 120,000; Full time

Job summary

Norton Rose Fulbright is establishing a new R&D capability in London to develop data-driven and AI-enabled solutions for legal services. The AI Engineer will deliver end-to-end AI features, from UI to backend and orchestration, while ensuring security, observability and maintainability.

You will collaborate with product, data, technology and security teams to translate designs into reliable products. You will join a small, hands-on team focused on discovery, prototyping, engineering and

Qualifications

• Hands-on full-stack AI engineering role for enterprise products.

Responsibilities

• Build complete AI-enabled product features across UI, backend and AI orchestration.
• Design and build agentic AI workflows with structured outputs and tool calls.
• Create reusable agent harnesses with runtime patterns and test fixtures.
• Develop grounded retrieval and context patterns using data products and document repos.
• Design and operate evaluation harnesses with automated and human grading.
• Manage prompts, model configuration, tool schemas and routing as product assets.
• Implement AI observability with traces, logs, metrics and evaluation results.
• Build APIs and integrate AI products with data, document, search and business systems.
• Create usable interfaces to show AI output, evidence, and controls to users.
• Write maintainable Python and TypeScript code; use Docker, Git, automated testing and CI/CD.
• Apply security, authentication and data-handling patterns with firm tech teams.

Tools

Python

TypeScript

Docker

Git

CI/CD

Job description

Practice Group / Department:

Legal Innovation, Design & Technology - London

Job Description

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.

With over 7,000 employees worldwide, our culture is the thread that connects us. Our strategy and culture are closed connected – defined by shared ambition, global collaboration and a one-team mindset. We believe pioneering work happens when people are empowered to think beyond boundaries, explore new opportunities and grow through diverse experiences. Alongside the right skills and experience, we are looking for people who are innovative, commercially minded, and motivated by the impact of the work they do – ready to share in our ambition and help shape what comes next.

Because while individuals can do well, together we achieve something extraordinary.

Role Purpose

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. You will build AI workflows, tools and integrations, evaluation and observability capabilities, together with the APIs, services and user interfaces needed to ship them reliably.

This is not a research-only role. You will apply agreed enterprise architecture, security and deployment patterns, while taking practical responsibility for code quality, testing, monitoring and troubleshooting with product, data, technology and security teams.

You will join a small, hands-on multidisciplinary team. Each team member will workcollaboratively across discovery, prototyping, engineering, productionisation and continuous improvement.

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

Initial Focus

• End-to-end agentic AI features and reusable harnesses for Radar and other named R&D products.

• A practical evaluation and test capability that lets the team compare prompts, models, tools and retrieval approaches before and after release.

• Grounded AI workflows that combine firm data, documents, market or regulatory information and user context into useful, attributable outputs.

• Interfaces that make AI output, confidence, evidence, controls and next actions clear to lawyers and business users.

What Success Looks Like

• R&D products use AI workflows that are grounded in trusted context, observable in operation and understandable to users.

AI quality is measured rather than assumed; evaluation results, traces and feedback lead to demonstrable improvements in usefulness, reliabili