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WTW

Global risk management, insurance brokerage, consulting

Lead AI Engineer

AI Application EngineeringPosted on Nov 9
Full-time

About the Job

Requirements

  • At least 10 years of professional software engineering experience, with a record of leading complex delivery as a hands-on technical authority and mentoring others through the work.
  • At least 2 years of recent hands-on experience building and shipping LLM-based and agentic systems to production as part of a delivery team, using retrieval-augmented generation, prompt engineering, and agent frameworks or orchestration such as LangGraph or Semantic Kernel, including evaluation, guardrails, and cost management.
  • Expert hands-on use of AI coding assistants and agentic development tools such as Claude Code, GitHub Copilot, or Cursor, with the discipline to critically review, validate, and secure their output.
  • Strong engineering capability in Python and at least one of C#/.NET or TypeScript (Angular and/or React), with hands-on commercial experience of cloud (Azure preferred), GitHub or Azure DevOps, and the software development lifecycle, continuous integration/continuous delivery, and Agile delivery.
  • A strong analytical and quantitative background, for example in mathematics, statistics, engineering, or computer science, with the ability to reason rigorously about data, models, and uncertainty.
  • Strong domain knowledge of insurance, reinsurance, or risk analytics, ideally actuarial or catastrophe modelling, or a record of becoming expert quickly in a complex quantitative field.
  • Ability to work directly with business users or clients, move a concept from conversation to working demonstration in days, and carry it into production; constructively challenge established approaches and clearly communicate complex trade-offs to senior stakeholders, actuaries, and engineers.
  • Ability to work independently and collaboratively through shared backlogs, code review, and handover to service teams.
  • Evidence of staying current at the frontier of AI, including recent tools, models, and techniques tested hands-on and conclusions drawn from them.

Responsibilities

  • Work as a forward deployed engineer within the delivery team, engaging with brokers, actuaries, modellers, and clients to build AI solutions across R&A and Data.
  • Prove ideas quickly with working software, then industrialise successful solutions through the delivery pipeline, including backlog, code review, continuous integration/continuous delivery, quality assurance, and release, into secure, supportable production services handed over to service teams.
  • Design and build agentic analytics and AI-enabled products using large language models, retrieval-augmented generation, Model Context Protocol, and modern agent frameworks, as reusable components and services on the shared platform.
  • Own the production quality of AI, including evaluation, testing, guardrails, observability, security, and cost management, to ensure AI answers over risk data can be trusted.
  • Apply quantitative expertise and understanding of insurance, exposure, loss, and catastrophe modelling to build solutions that stand up to scrutiny from actuaries and modellers.
  • Plan and prioritise with Delivery Leads and Product Leads, and shape the technical direction for AI with Product Architects within architecture, security, and compliance standards.
  • Direct AI coding assistants and agentic workflows, including Claude Code and GitHub Copilot, and rigorously review human-written and AI-generated code against standards.
  • Raise team standards through the quality and pace of individual work, informally mentor colleagues, and share learning to spread capability.
  • Track new models, tools, and techniques, test them hands-on, and bring credible approaches into the roadmap and delivery model.

Desired Qualifications

  • Experience designing multi-agent workflows and building reusable Model Context Protocol servers or tools that expose enterprise data and calculations to AI agents.
  • Experience running AI or large language model solutions at scale in a regulated environment, including evaluation frameworks, model governance, and responsible AI practices.
  • Experience with the platform stack (C#/.NET, Angular or React, Azure), microservice architectures, and containerisation.
  • Experience with geospatial, catastrophe, or financial-modelling data, or building mathematical algorithms.
  • Earlier applied machine learning, statistics, or data science experience alongside recent work with large language models.
  • Visible contribution to the AI engineering community through open-source work, writing, or speaking.
  • A degree-level qualification in a quantitative discipline (Mathematics, Science, Engineering, or Computer Science); postgraduate study is valued.

About the Company

WTW

Global risk management, insurance brokerage, consulting

Willis Towers Watson provides risk management, advisory, and brokerage services to organizations worldwide. Its two segments are Risk & Broking, which identifies, quantifies, and places insurance coverage, and Health, Wealth & Career, which offers consulting, technology, and administration for health benefits, retirement plans, and talent programs. The company blends advisory services, technology platforms, and brokerage capabilities to deliver integrated risk and people solutions rather than relying on a single service line. Its goal is to help clients turn risk into a path for growth by aligning risk management with health, retirement, and talent strategies to support organizational success.

Company size
10,001+
Company stage
IPO
Headquarters
London, United Kingdom
Founded
1828

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