Senior Lead AI Engineer

About the role

Oliver Wyman, a business of Marsh, is looking for a senior technology leader to 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, requiring strong software architecture and delivery judgment and the credibility to guide engineers and senior stakeholders from opportunity definition through production adoption.

Key responsibilities

• Lead AI solution delivery, owning the technical journey from problem framing and feasibility through architecture, implementation, release and continuous improvement.
• Design pragmatic architectures connecting AI capabilities with existing applications, APIs, data platforms, identity, security and operational controls.
• Build and guide hands-on engineering, develop or review critical components and set coding and testing standards.
• Design and implement agentic applications using agentic harnesses, RAG, MCP, function/tool calling, structured outputs and multi-agent workflows.
• Engineer AI platforms: inference architectures, model routing, caching, semantic retrieval, vector databases, evaluation pipelines, observability and cost optimization.
• Define evaluation criteria, test sets and feedback loops for accuracy, reliability, safety, latency, cost and business impact.
• Work with security, privacy, risk and legal partners on guardrails, human oversight, access controls and auditability.
• Mentor engineers, share reusable patterns and reference implementations, and communicate solution options and risks to technical and non-technical stakeholders.

Required profile

• Typically 10-12 years of professional experience in software engineering, solution architecture, platform engineering or related enterprise technology roles.
• Record of delivering production enterprise applications, integrations or digital platforms in complex, regulated or security-conscious environments.
• Recent hands-on experience with AI-enabled solutions such as agentic applications, RAG, intelligent workflow automation or machine-learning services.
• Experience leading technical work across multidisciplinary teams and mentoring engineers.
• Useful but not essential: consulting, professional services or client-facing delivery experience, regulated industry experience, familiarity with AI application frameworks or model platforms, and experience with Kubernetes and infrastructure as code.
• Degree in computer science, engineering or a related discipline, or equivalent professional experience.

Required skills

• Software engineering fundamentals: API design, distributed systems, automated testing, version control, CI/CD, observability and secure development.
• Python, and at least one enterprise stack such as Java.NET or TypeScript/Node.js.
• Experience with at least one major cloud platform (AWS, Microsoft Azure or Google Cloud).
• Data integration, SQL, search and retrieval, and structured/unstructured information handling.
• Prompt and context design, model and application evaluation, and production monitoring.