AI Engineer

NRF United Kingdom; Greater London; Hybrid; GBP 90,000 - 130,000; Full time

Job summary

Norton Rose Fulbright is expanding its Legal Innovation, Design & Technology team in London to deliver AI-enabled legal services. The AI Engineer will ship end-to-end AI workflows, integrate tools and data sources, and ensure security and observability across the product lifecycle.

You will work with Python, TypeScript, and modern AI tooling in a hands-on, production-focused environment within a multidisciplinary team in London.

Qualifications

• At least three years delivering production AI products or workflows.
• Experience with SaaS AI features and integration into enterprise systems.
• Strong Python and frontend basics (TypeScript/JS).
• Experience using LLMs, tool calling, retrieval and embeddings.

Responsibilities

• Build end-to-end AI-enabled product features across UI and backend.
• Design and implement agentic AI workflows with tool calls and retrieval.
• Develop observable, testable AI products with secure data handling.
• Collaborate with security, data, and product teams to ship reliably.

Skills

Python

TypeScript

LLM APIs

Agentic AI

AI Observability

CI/CD

Tools

FastAPI

Flask

React

Next.js

Docker

Git

Job description

Practice Group / Department: Legal Innovation, Design & Technology - 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. 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 work collaboratively 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, reliability, latency and cost. The engineer can independently take a well‑scoped AI workflow from problem definition to controlled deployment. At least one AI component, harness or evaluation capability is reused by a second R&D product or workflow. Promising prototypes have a repeatable path to controlled release, monitoring and improvement.

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. Experience in legal technology or a law firm is not required, but would be advantageous. 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 building agentic systems using an agent framework or custom orchestration patterns, including tools, state/context management and human‑review or approval controls. 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. Experience designing APIs and integrating with enterprise systems; sound handling of authentication, authorisation, secrets and sensitive data. Comfort with Docker, Git, automated testing and CI/CD. You do not need to be a dedicated platform engineer, but should be able to ship maintainable, repeatable work. Strong debugging and systems‑thinking skills, including the ability to diagnose failures across models, prompts, tools, retrieval and integrations. Able to explain AI behaviour, evaluation results and technical trade‑offs clearly to technical and non‑technical colleagues.

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. We offer a range of family friendly and inclusive employment policies and provide access to programmes and services aimed at nurturing our people’s health and overall wellbeing. Find more about Diversity, Equity and Inclusion here. We are proud to be an equal opportunities employer and encourage applications from individuals who can complement our existing teams. We strive to create an inclusive and accessible recruitment process for all candidates. If you require any tailored adjustments or accommodations, please let us know here.

Norton Rose Fulbright provides a full scope of legal services to the world’s preeminent corporations and financial institutions. The global law firm has more than 3,000 lawyers advising clients across locations in the United States, Europe, Canada, Latin America, Asia, Australia, Africa and the Middle East. With its global business principles of quality, unity and integrity, Norton Rose Fulbright is recognized for its client service in key industries, including financial institutions; energy, infrastructure and resources; technology; transport; life sciences and healthcare; and consumer markets.