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.
Skills Required
- 4-5 years of experience in AI engineering, machine learning, data science, software engineering, data engineering, or related technology field
- Designing, developing, and deploying AI, machine learning, or Generative AI solutions in production environments
- Hands-on experience with large language models (LLMs), prompt engineering, RAG, and model evaluation
- Experience building production LLM-powered applications and agentic workflows using orchestration frameworks
- Experience with LangChain, LangGraph, LlamaIndex, CrewAI, or similar tools
- Experience building and managing scalable data pipelines, ETL/ELT processes, and data integration workflows
- Experience with relational, NoSQL, and vector databases to support enterprise AI and analytics
- Experience deploying solutions on cloud platforms such as AWS, Microsoft Azure, and/or Google Cloud Platform
- Familiarity with software engineering best practices including API development, microservices, version control, automated testing, CI/CD, and containerization
- Experience with MLOps and/or LLMOps practices including model lifecycle management, monitoring, experimentation tracking, deployment, and observability
- Ability to translate business requirements into technical solutions and work with cross-functional teams; strong communication skills
- Exposure to responsible AI, model governance, AI risk management, security controls, and regulatory considerations
- Consulting, professional services, or client-facing delivery experience
Marsh McLennan Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Marsh McLennan and has not been reviewed or approved by Marsh McLennan.
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Leave & Time Off Breadth — Leave offerings are described as generous, including sizable PTO, paid holidays, paid sick days, and additional time off such as paid volunteer time and “Summer days.” These time-off benefits are portrayed as a standout part of the overall rewards package.
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Healthcare Strength — Healthcare coverage is characterized as comprehensive, spanning medical, dental, and vision options, with additional supports like disability and life insurance and access to mental health resources and an EAP. The breadth of plan options is positioned as a core strength of the benefits package.
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Retirement Support — Retirement benefits are framed as solid, with 401(k) programs and employer matching frequently highlighted alongside other financial programs. Stock purchase options are also referenced as an additional wealth-building component of the total rewards mix.
Marsh McLennan Insights
What We Do
Marsh McLennan (NYSE: MMC) brings together nearly 78,000 experts in risk, strategy, and people across Marsh, Guy Carpenter, Mercer, and Oliver Wyman, serving clients in over 130 countries. Marsh enables enterprise worldwide by helping clients manage risks, transforming uncertainty into opportunity. Guy Carpenter helps clients grow profitably with reinsurance broking expertise, advisory services, and advanced analytics. Mercer helps organizations advance the health, wealth, and careers of their most vital asset — their people. Oliver Wyman’s expertise in strategy, operations, risk, and organization transformation changes what is possible for our clients, their industries, and society. Together, we combine a unique range of capabilities to help our clients solve problems, seize opportunities, and build lasting success in increasingly complex operating environments.








