About the Team
The AI and Engineering team develops practical AI, automation and integration capability for legal practice and business operations. It works closely with Product, Data, Architecture, Security and operational teams to turn valuable ideas into secure, maintainable services, while retaining space for controlled experimentation and rapid learning. Reporting to the AI & Innovation Lead, the Applied AI Engineer builds and improves AI-enabled products and workflows.
Responsibilities
•Work against the prioritised AI and innovation roadmap, with the Product Manager accountable for product priorities and outcomes and the AI & Innovation Lead accountable for innovation direction and team capability.
•Build AI-enabled applications, agents and workflow components using Python, TypeScript, Azure and approved model services.
•Develop prompts, system instructions, tool definitions, retrieval approaches and context strategies suited to real legal and operational tasks.
•Design and evaluate retrieval pipelines, including document preparation, chunking, metadata, permissions-aware access, source attribution and retrieval quality.
•Build MCP integrations and other APIs that allow agents to use approved data and tools through controlled interfaces.
•Use AI-assisted software development to accelerate prototyping, while reviewing, testing and hardening generated code before release.
•Create representative evaluation sets from real use cases and define acceptance thresholds before wider release.
•Evaluate end-to-end outcomes and intermediate agent behaviour, including tool selection, task adherence, groundedness, safety and failure modes.
•Measure token consumption, latency and cost; compare models and architectures and recommend the simplest approach that meets the need.
•Use telemetry and user feedback to improve prompts, retrieval, skills, agents and model configuration after release.
•Track task-success rate, output quality, human correction or override, latency, unit cost, adoption and time or capacity created.
•Work with Product to turn opportunities into clear hypotheses, technical spikes, PoCs and controlled pilots.
•Document experiment design, limitations, decisions and results so learning can be reused across the roadmap.
•Retain engineering ownership as successful prototypes move into secure, monitored and supportable production services, working with the Automation & AI Engineer on architecture, integration and production standards.
•Contribute reusable components, evaluation harnesses, prompt patterns and engineering guidance.
•Protect confidential and personal data through appropriate access, retention and environment controls.
•Design human review, fallback and escalation into AI workflows where quality or consequence requires it.
•Explain model limitations and uncertainty clearly, avoiding the presentation of experimental capability as production-ready.
•Monitor production AI services for quality, cost, latency, tool failures and unexpected behaviour; investigate incidents and maintain appropriate runbooks, rollback arrangements and operational guidance.
Requirements
•Strong software engineering fundamentals, including version control, testing, code review, documentation and secure development.
•Experience working with APIs, integrations, cloud services and event-driven or workflow-based solutions.
•Ability to move from an unclear problem to a working prototype, then improve it using evidence and user feedback.
•Confidence working with Product, Data, Architecture, Security and business specialists in a multidisciplinary environment.
•Clear written and verbal communication, including the ability to explain technical trade-offs to non-technical stakeholders.
•A pragmatic approach to experimentation, with good judgement about what is safe to test and what is ready to operate.
•Use of approved identity, secrets-management and service-connection patterns, avoiding embedded credentials and unmanaged service accounts.
•Commitment to accessible and usable user-facing applications and workflow interfaces.
•Hands-on experience building LLM applications or agents, including prompts, tool use, retrieval and structured outputs.
•Strong Python capability and working knowledge of TypeScript or JavaScript.
•Practical experience evaluating generative AI systems using test data, traces and measurable quality criteria.
•Understanding of model selection, context windows, token consumption, latency and consumption-based cost.
•Curiosity and technical depth across agents, skills, MCP and emerging AI engineering patterns.
Nice to Have
•Experience in legal services, professional services or another regulated, knowledge-intensive environment.
•Experience with Azure, Git-based delivery, CI/CD, telemetry and production support.
•A strong portfolio of working software, prototypes or automation is more important than a specific degree.
•Experience with Microsoft Foundry, Azure OpenAI, vector search, agent frameworks or Copilot Studio.
•Experience applying AI to document-heavy, high-stakes or professional workflows.
•Familiarity with model risk, content safety and human-in-the-loop controls.
Conditions
•Hybrid working in London.
•Agile working culture, open to discussing flexible working arrangements.
