Description
Norton Rose Fulbright is 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, including agentic workflows, retrieval and context, agent and evaluation harnesses, and user-facing features.
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 calls, workflow orchestration, document processing, human-review steps and appropriate controls.
- Build reusable agent harnesses and practical runtime patterns around tools, state, context, approvals, traces and test fixtures.
- Implement AI observability through traces, logs, metrics and evaluation results.
- Build clear APIs and integrate AI products with approved data, document, search and business systems.
- Write maintainable Python and TypeScript code; use Docker, Git, automated testing and CI/CD.
The engineer will work collaboratively across discovery, prototyping, engineering, productionisation and continuous improvement.
Success metrics:
- 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.
- The engineer can independently take a well-scoped AI workflow from problem definition to controlled deployment.
Essential skills and experience:
- At least three years' professional experience delivering production AI products.
- Strong Python, plus practical TypeScript or JavaScript.
- Demonstrable experience building AI-enabled products or workflows using LLM APIs, structured outputs, retrieval and embeddings.
- Experience building agentic systems using an agent framework or custom orchestration patterns.
- Experience evaluating AI workflows using representative test sets, scenario design, model or prompt experiments.
- Experience taking AI features from prototype into controlled production.
- Strong understanding of grounded retrieval, context quality, evidence/citations and AI system failure modes.