Lead AI Engineer

WTW•New York, NY

17h•$100,000 - $350,000•Hybrid

About The Position

The Lead AI Engineer is a senior, high-autonomy individual contributor on WTW’s Willis Neuron Technology team, based in New York or London. Working as a forward deployed engineer within the delivery team, the role partners with brokers, actuaries, catastrophe modellers, architects, engineers and clients to identify valuable problems, rapidly demonstrate solutions, and take successful ideas through to secure, tested, observable and supportable production services on a shared platform. This is not a line-management role. The engineer designs and builds agentic analytics and AI-enabled products using large language models, RAG, MCP and modern agent frameworks. They establish quality standards for AI in production, including evaluation, testing, guardrails, observability, security and cost management, and apply quantitative and insurance-risk knowledge to solutions that withstand scrutiny. The role contributes to technical direction, uses and reviews AI coding tools and generated code, mentors colleagues informally, and evaluates emerging models and techniques for adoption. Hybrid working is offered, with regular office attendance for meetings and workshops and occasional stakeholder travel.

Requirements

At least 10 years of professional software engineering experience in a modern technology stack, with experience leading complex delivery as a hands-on technical authority and mentoring others.
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, including RAG, prompt engineering, agent frameworks or orchestration, evaluation, guardrails and cost management.
Expert hands-on use of AI coding assistants and agentic development tools, with the ability to critically review, validate and secure their output.
Strong Python skills and proficiency in at least one of C#/.NET or TypeScript (Angular and/or React).
Commercial hands-on experience with cloud (Azure preferred), GitHub or Azure DevOps, SDLC, CI/CD and Agile delivery.
Strong analytical and quantitative background, such as mathematics, statistics, engineering or computer science, and ability to reason rigorously about data, models and uncertainty.
Strong 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 from discussion to working demonstration in days, and carry concepts into production.
Ability to challenge established approaches constructively and communicate complex trade-offs clearly 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 with AI tools, models and techniques through hands-on testing and considered conclusions.

Nice To Haves

Experience designing multi-agent workflows and building reusable MCP servers or tools that expose enterprise data and calculations to AI agents.
Experience operating AI/LLM 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 LLM work.
Visible contribution to the AI engineering community, such as open-source work, writing or speaking.
Degree-level qualification in a quantitative discipline such as Mathematics, Science, Engineering or Computer Science; postgraduate study is valued.

Responsibilities

Work as a forward deployed engineer, engaging closely with brokers, actuaries, modellers and clients to build AI solutions across R&A and Data.
Rapidly prove ideas with working software, then take successful solutions through backlog, code review, CI/CD, QA and release into secure, supportable production services, with clean handover to service teams.
Design and build reusable agentic analytics and AI-enabled products using LLMs, RAG, MCP and modern agent frameworks on the shared platform.
Own production AI quality, including evaluation, testing, guardrails, observability, security and cost management.
Apply quantitative expertise and insurance, exposure, loss and catastrophe-modelling knowledge to develop robust solutions.
Plan and prioritise with Delivery Leads and Product Leads, and shape AI technical direction with Product Architects within architecture, security and compliance standards.
Use AI coding assistants and agentic workflows to increase delivery output, and rigorously review human-written and AI-generated code against standards.
Lead by example through high-quality, timely work; informally mentor colleagues and share learning.
Track, test and assess emerging AI models, tools and techniques, and bring credible options into the roadmap and delivery approach.

Benefits

Annual short-term incentive bonus
Medical insurance, including prescription coverage
Dental insurance
Vision insurance
Health Savings Account
Commuter Account
Health Care and Dependent Care Flexible Spending Accounts
Group Accident coverage
Group Critical Illness coverage
Life Insurance
AD&D coverage
Group Legal
Identity Theft Protection
Wellbeing Program
Work/Life Resources, including Employee Assistance Program
Paid Holidays
Annual Paid Time Off, including paid state/local leave where required
Short-Term Disability
Long-Term Disability
Other leave benefits, including bereavement, FMLA, ADA, jury duty, military, parental and adoption leave
Contributory Pension Plan
401(k) Savings Plan
Some Level 38 and more senior roles may be eligible for non-qualified Deferred Compensation and Deferred Savings Plans