The role contributes to WTW's central, enterprise-wide AI engineering capability by building, testing, documenting, and supporting moderately complex components of shared AI frameworks, reusable patterns, and reference implementations. Working with senior engineers, Architecture, Information Security, lines of business, and business users, you will apply broad AI engineering knowledge to help teams develop AI solutions safely, consistently, and at scale.

We are seeking a motivated AI Engineer with practical hands-on experience developing generative AI and agentic solution components across enterprise AI platforms, model providers, and orchestration frameworks. The role is expected to contribute to standard technical projects, address routine technical challenges using established practices, escalate more complex issues appropriately, and collaborate effectively across related teams. Relevant experience may include Microsoft Foundry, Copilot Studio, Lyzr, Anthropic Claude, Moveworks, or similar technologies. We are open to candidates working in a 'remote' or 'hybrid' pattern located in the US or Great Britain, with occasional domestic or international travel as required.

The Role

• Build, test, document, and support moderately complex components of WTW's central AI engineering frameworks, contributing to reusable foundations used by lines of business.
• Develop generative AI and agentic solution components across enterprise AI platforms, model providers, and orchestration frameworks, including but not limited to:
  • Enterprise AI development and orchestration platforms, including agent frameworks, declarative and custom-engine agents, plugins, skills, connectors, and extensions
  • Large language models and model-provider APIs, including tool use, governed integration patterns, and protocols such as the Model Context Protocol
  • Moveworks and other agentic automation and employee-support platforms
• Apply shared patterns, reference implementations, SDKs, and reusable components including prompt libraries, retrieval pipelines, and tool/connector catalogues, escalating design questions or exceptions to senior engineers and architects.
• Work with lines of business and business stakeholders to understand defined use cases, support workflow assessment, prototype solution components, and validate outcomes against agreed requirements.
• Implement governance, responsible-AI, and security controls under established standards, including identity, data protection, content safety, and auditability.
• Build and support context engineering, retrieval-augmented generation (RAG), and agent orchestration components, integrating enterprise data sources through approved secure connectors.
• Apply LLMOps practices such as evaluation, testing, prompt and configuration versioning, monitoring, and observability to support quality, reliability, and cost awareness in production.
• Assist with technical support for AI services by investigating routine incidents, applying established troubleshooting practices, documenting findings, and escalating complex production issues as appropriate.
• Coordinate defined changes for AI capabilities, keeping relevant teams informed of progress, impacts, and implementation details.
• Create and maintain practical technical documentation, including design notes, runbooks, implementation guidance, and support information for consuming the central AI frameworks.
• Identify incremental opportunities for engineering and operational improvement, contribute recommendations, and support implementation of agreed improvements.
• Share knowledge with peers and junior colleagues, support a collaborative engineering environment, and follow established engineering practices and standards.
• Contribute to evaluation of emerging AI technologies, tools, and platforms by researching options, testing defined scenarios, and summarising findings for senior review.
• Maintain a strong information security and responsible-AI posture by following data protection, model governance, and applicable regulatory requirements.
• Develop effective working relationships with team members, related teams, and stakeholders, using clear communication to support collaboration and task completion.