Marsh’s purpose is to shape the future through perspective, expertise and solutions, empowering clients to thrive. With a foundation built over 150 years, Marsh brings clarity to complexity through industry knowledge and data-driven insights, and builds trusted relationships grounded in integrity and service.
Marsh is seeking an Applied AI Product Manager to help translate advances in AI into useful, reliable, production-ready products. This role sits at the intersection of customer discovery, product strategy, technical execution, and responsible deployment. You will partner across engineering, design, data, research, go-to-market, legal, and customer-facing teams to identify high-value workflows, prototype AI-powered experiences, define quality standards, and deliver measurable business and user impact.
We will count on you to:
* Own product strategy, roadmap, and execution for applied AI capabilities across one or more product areas.
* Identify workflows where AI can materially improve speed, quality, decision-making, automation, or user experience.
* Translate ambiguous customer problems into clear product requirements, success metrics, prototypes, and release plans.
* Partner with engineering and AI teams on LLM-powered systems, including prompts, context design, retrieval, tool use, agents, model selection, and product architecture tradeoffs.
* Define and run practical evaluation processes for AI features, including quality rubrics, offline evaluations, regression tests, online metrics, user feedback loops, and human review.
* Make product decisions across accuracy, latency, cost, reliability, safety, privacy, and user trust.
* Use modern AI tools and coding/productivity harnesses to accelerate research, prototyping, analysis, QA, and product thinking.
* Monitor shipped AI experiences for quality, failure modes, adoption, business impact, and operational risk.
* Collaborate with design to create intuitive AI UX, including graceful failure states, user control, transparency, and feedback mechanisms.
* Work with legal, security, compliance, and policy partners to support responsible deployment.
* Enable sales, support, customer success, and marketing with clear positioning, demos, launch materials, and customer narratives.
What you need to have:
* 2+ years of experience building, shipping, implementing, analyzing, or operating software, data, AI, automation, or technical products.
* Strong product judgment and the ability to turn fuzzy problems into sharp product bets.
* Demonstrated ability to build, ship, prototype, or meaningfully contribute to technical products.
* Familiarity with modern AI tools and workflows, including hands-on use of AI assistants, coding agents, or product/research harnesses.
* Working knowledge of LLM product patterns: prompt design, context engineering, tool/function calling, agents, evaluation, failure modes, and when retrieval or external knowledge sources are useful.
* A strong evaluation mindset: you can define what “good” means, measure quality, find regressions, and connect AI performance to user and business outcomes.
* Technical fluency with APIs, data, product analytics, and system tradeoffs (SQL, Python, or lightweight scripting is helpful but not required).
* Ability to distinguish between a model problem, data problem, UX problem, workflow problem, and expectation-setting problem.
* Clear communication with both technical and non-technical audiences.
* High ownership and comfort working in ambiguity.
What makes you stand out:
* Experience shipping AI, ML, automation, data, developer, or workflow products.
* Experience creating and running evaluations for AI systems (test sets, grading rubrics, failure analysis, regression tracking, human review workflows).
* Experience with observability, quality dashboards, annotation workflows, or human-in-the-loop systems.
* Experience with enterprise AI, regulated industries, privacy-sensitive products, or compliance-heavy environments.
* Familiarity with retrieval systems, search, knowledge management, or other ways of giving AI systems useful context.
* Background in engineering, data science, research, technical consulting, design, or founder-like product work.
Skills Required
- 2+ years of experience building, shipping, implementing, analyzing, or operating software, data, AI, automation, or technical products
- Strong product judgment and ability to turn ambiguous problems into clear product opportunities
- Demonstrated ability to build, ship, prototype, or meaningfully contribute to technical products
- Hands-on familiarity with modern AI tools and workflows, including AI assistants, coding agents, or product and research harnesses
- Working knowledge of LLM product patterns, including prompt design, context engineering, tool calling, agents, evaluation, failure modes, retrieval, and external knowledge sources
- Ability to define quality standards, measure AI performance, identify regressions, and connect results to user and business outcomes
- Technical fluency with APIs, data, product analytics, and system tradeoffs
- Ability to distinguish model, data, UX, workflow, and expectation-setting problems
- Clear communication with technical and non-technical audiences
- High ownership and comfort working in ambiguity
- Experience shipping AI, machine learning, automation, data, developer, or workflow products
- Experience creating and operating evaluations for AI systems, including test sets, grading rubrics, failure analysis, regression tracking, or human review workflows
- Experience with observability, quality dashboards, annotation workflows, or human-in-the-loop systems
- Experience with enterprise AI, regulated industries, privacy-sensitive products, or compliance-heavy environments
- Familiarity with retrieval systems, search, or knowledge management
- Background in engineering, data science, research, technical consulting, design, or founder-like product work
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.






