Senior Applied Artificial Intelligence (AI) Engineer (Insurance Domain Experience)

Milliman•Dallas, TX

19h•$104,900 - $199,065•Remote

About The Position

Milliman AI Solutions is establishing a new practice to leverage artificial intelligence (AI) for enhancing operational efficiency and delivering innovative client solutions. This practice aims to accelerate development, manage risk, and capitalize on emerging technologies. The Senior Insurance Applied AI Engineer will play a key role in building and refining the business engagement and engineering capability within this practice, responding to the growing demand for AI transformation services. This role requires a blend of insurance domain expertise, business consulting skills, and applied AI fluency to translate complex client workflows into practical AI-enabled transformation opportunities. The individual will collaborate with practice leaders, domain experts, AI engineers, and architects to map and redesign workflows, capture expert knowledge, identify value creation through enterprise AI tools and agentic systems, and shape AI implementation pathways. The potential exists for this role to evolve into a leadership position within the AI Solutions team, focusing on refining engagement models, developing reusable assets, and enabling a scalable capability.

Requirements

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.
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.
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.
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.
Working fluency with LLMs, prompt design, retrieval-augmented generation, knowledge assistants, agentic workflows, evaluation approaches, human-in-the-loop design, and responsible AI principles.

Responsibilities

Support practices in responding to client demand for AI transformation capabilities, including opportunity shaping, diagnostic workshops, proposal development, and AI use case development.
Translate client strategic priorities, target operating models, and workflow pain points into actionable AI transformation roadmaps, use case portfolios, and AI implementation pathways.
Help clients move beyond isolated AI pilots by connecting AI use cases to measurable business value, adoption requirements, and scalable delivery.
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.
Co-design practical AI-enabled solutions with business experts, AI engineers, and product owners, ensuring grounding in real workflows and implementation constraints.
Advise clients and internal teams on the effective use of enterprise AI tool ecosystems, including generative AI tools, knowledge assistants, agentic workflow platforms, document intelligence, evaluation methods, and responsible AI controls.
Define requirements, success measures, validation approaches, adoption considerations, and handoff materials for AI engineers to move from concept to scalable implementation.