Applied AI Product Manager

Company:

Marsh

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.

Mars…