Oliver Wyman - Senior Lead AI Engineer - Singapore

Company:

Description:

Who we are

At Oliver Wyman, a Marsh (NYSE: MRSH) business, we bring deep industry insight, bold innovation, and a collaborative approach that cuts through complexity to help organizations navigate their most defining transformative moments. 

 
As a business of Marsh, we work alongside the world’s leading experts across risk, reinsurance and capital, people and investments, and management consulting. Together with Marsh Risk, Guy Carpenter, and Mercer, we help organizations build resilience and competitive advantages from every angle. With annual revenue of $27 billion and more than 95,000 colleagues in 130 countries, Marsh helps build the confidence to thrive through the power of perspective. 

For more information, visit oliverwyman.com.   

The opportunity 

We are looking for a senior technology leader who has built and modernized enterprise applications and now applies that engineering foundation to AI-enabled products. You will lead the design and delivery of secure, reliable AI solutions that integrate with real business processes, data and technology estates. 

This is an applied engineering role, not a research-science position. Success requires strong software architecture and delivery judgment, practical experience with modern AI capabilities, and the credibility to guide engineers and senior stakeholders from opportunity definition through production adoption. 

What you will do

• Lead AI solution delivery. Own the technical journey from problem framing and feasibility assessment through architecture, implementation, production release and continuous improvement. 

• Design for the enterprise. Create pragmatic architectures that connect AI capabilities with existing applications, APIs, data platforms, identity, security and operational controls. 

• Build and guide hands-on engineering. Develop or review critical components, establish coding and testing standards, and help teams make sound trade-offs across quality, cost, speed and maintainability. 

• Build production AI systems. Design and implement agentic applications using agentic harnesses, retrieval-augmented generation (RAG), MCP, function/tool calling, structured outputs, and multi-agent workflows where appropriate.
• Engineer AI platforms for production. Design inference architectures, model routing, caching, semantic retrieval, vector databases, evaluation pipelines, observability, and cost optimization to support enterprise-scale AI applications.

• Make quality measurable. Define evaluation criteria, test sets, observability and feedback loops for accuracy, reliability, safety, latency, cost and business impact. 

• Embed responsible delivery. Work with security, privacy, risk and legal partners to implement suitable guardrails, human oversight, access controls and auditability. 

• Partner across disciplines. Collaborate with product owners, business leaders, architects, data specialists and delivery teams to turn priorities into achievable roadmaps. 

• Raise engineering capability. Mentor engineers, contribute reusable patterns and reference implementations, and help teams adopt effective AI engineering practices. 

• Communicate with influence. Explain solution options, risks and recommendations clearly to both technical and non-technical stakeholders, including senior client or business leaders. 

What you will bring

• Typically 10-12 years of professional experience in software engineering, solution architecture, platform engineering or related enterprise technology roles. 

• A strong record of designing and delivering production enterprise applications, integrations or digital platforms in complex, regulated or security-conscious environments. 

• Recent hands-on experience implementing AI-enabled solutions, such as agentic applications, retrieval-augmented generation, intelligent workflow automation or machine-learning services. 

• Strong software engineering fundamentals, including API design, distributed systems, automated testing, version control, CI/CD, observability and secure development practices. 

• Proficiency in Python and practical experience with at least one enterprise application stack such as Java, .NET or TypeScript/Node.js. 

• Experience delivering on at least one major cloud platform. Depth in one environment is more important than superficial coverage of AWS, Microsoft Azure and Google Cloud. 

• Working knowledge of enterprise data integration, SQL, search/retrieval and the handling of structured and unstructured information; specialist data-platform engineering is not required. 

• Practical understanding of model and application evaluation, prompt and context design, privacy, security, responsible AI controls and production monitoring. 

• Experience leading technical work across multidisciplinary teams, mentoring engineers and influencing architecture or engineering standards without relying solely on formal authority. 

• Clear communication, commercial judgment and the ability to translate ambiguous business needs into a feasible technical approach and delivery plan. 

Useful but not essential

• Consulting, professional services or client-facing technology delivery experience.
• Experience in a regulated industry or with enterprise risk, compliance and governance processes.
• Familiarity with one or more AI application frameworks (e.g. Langraph, PydanticAI) or model platforms (e.g. Azure Foundry/AWS Bedrock). We value sound engineering decisions over experience with a specific vendor or framework.
• Experience operating containerized workloads or collaborating with platform teams using Kubernetes and infrastructure as code.
• A degree in computer science, engineering or a related discipline—or equivalent professional experience.