NOTE ABOUT LOCATION: This is a hybrid role out of Milliman’s Paris office. The ideal candidate must be flexible in working out of Milliman's Paris office. NOTE ABOUT THE SALARY RANGE: The overall range for this role is €59,400-€98,000 BackgroundThe rapid evolution of artificial intelligence (AI) presents a transformative opportunity for Milliman to enhance operational efficiency and deliver innovative client solutions. To accomplish this goal, a new practice, named Milliman AI Solutions, was established in January 2026. This practice ensures focused investment, governance, and accountability, enabling the firm to accelerate development, manage risk, and capitalize on emerging technologies. This initiative reflects a commitment to building a sustainable, business-driven AI capability that aligns with Milliman’s long-term growth objectives. Role PurposeMilliman AI Solutions is seeking a Senior Insurance Applied AI Engineer to establish and refine the business engagement and engineering capability within the practice. This role will help respond to increasing internal and client demand for AI transformation services, bridging expert workflow redesign, AI solution co-design, and implementation readiness. This role is intended for an experienced professional who combines insurance domain understanding, business consulting skills, applied AI fluency, and the ability to translate complex client workflows into practical AI-enabled transformation opportunities. The individual will support practices – Milliman’s consulting and product businesses – in client conversations, proposal development, diagnostic workshops, use case prioritization, AI solution framing and prototyping, and the transition from AI ambition to tangible business value. Working closely with practice leaders, domain experts, AI engineers and AI architects, the Senior Insurance Applied AI Engineer will map and redesign workflows, capture expert knowledge, identify where enterprise AI tools and agentic systems can create value, and shape AI implementation pathways that are credible and aligned with practices and client operating realities. Over time, the person taking on this role should have the potential to operate as a lead in the business engagement and engineering team within AI Solutions, refining its engagement model, developing reusable methods and assets, coordinating demand across practices, and enabling a scalable capability that connects client transformation needs with Milliman’s AI design and delivery expertise. Key Responsibilities Internal & Client AI Business Engagement - Support practices in responding to increasing client demand for AI transformation capabilities, from early opportunity shaping through diagnostic workshops, proposal development, and AI use case development engagements. - Translate client strategic priorities, target operating model, and workflow pain points into actionable AI transformation roadmaps, use case portfolios, and AI implementation pathways. - Help internal and external clients move beyond isolated AI pilots by connecting AI use cases to measurable business value, adoption requirements, and delivery at scale. Workflow Mapping, Redesign & Expert Knowledge Capture - Map current-state insurance workflows, decision processes, handoffs, controls, data flows, expert reasoning patterns, and pain points across actuarial, underwriting, claims, reporting, compliance, and related business domains. - Redesign target workflows around AI-enabled delivery, balancing automation, expert judgment, human oversight, governance, auditability, and change management. - Capture and structure expert knowledge into AI-ready artifacts, including reasoning workflows, mental maps, process documentation, prompt patterns, knowledge bases, evaluation criteria, and requirements for agentic workflows. AI Solution Co-Design & Enterprise Tool Enablement - Co-design practical AI-enabled solutions with business experts, AI engineers, and product owners, ensuring that AI solutions are grounded in real workflows and implementation constraints. - Advise clients and internal teams on the effective use of enterprise AI tool ecosystems, including approved generative AI tools, knowledge assistants, agentic workflow platforms, document intelligence capabilities, evaluation methods, and responsible AI controls. - Define requirements, success measures, validation approaches, adoption considerations, and handoff materials that enable AI engineers to move from concept to prototype, MVP, and scalable implementation. Expected Capabilities & Tooling Fluency - Insurance workflows mapping and optimization: Strong understanding of insurance workflows and the ability to engage credibly with domain experts across actuarial, underwriting, claims, reporting, compliance, risk, or adjacent functions. Demonstrated ability to map business processes, structure expert knowledge, identify improvement opportunities, and translate ambiguous business problems into practical solution requirements. - Delivery collaboration: Ability to work with domain experts, AI engineers and Technology teams on requirements, prototype framing, evaluation, architecture considerations, security constraints, deployment options, and production-readiness gaps. - Enterprise AI tool ecosystem: Practical understanding of how business users, hybrid users, and technical teams apply enterprise AI tools across chat, coding, active agents, workflow automation, document intelligence, data analysis, and prototyping environments. - Generative AI and agentic systems: Working fluency with LLMs, prompt design, retrieval-augmented generation, knowledge assistants, agentic workflows, evaluation approaches, human-in-the-loop design, and responsible AI principles. Qualifications: - Bachelor’s or Master’s degree in Actuarial Science, Engineering, Computer Science, Applied Mathematics, Data Science, Business, Finance, Insurance, or a related quantitative or business field. - 6–8 years of relevant professional experience in insurance, actuarial consulting, business transformation, operations, data management, technology-enabled consulting, or related fields. - Experience supporting business development, proposal writing, client workshops, solution scoping, or transformation roadmap development. - Excellent communication and stakeholder management skills, with the ability to bridge senior business leaders, subject matter experts, AI engineers, Technology teams, and client delivery teams. - Entrepreneurial mindset, structured problem-solving, commercial awareness, and willingness to build a new capability within a fast-evolving AI Solutions practice.