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 €45,500-€72,700 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 an AI Engineer to help translate operational workflows, domain knowledge, expert-driven reasoning, business problems, and user needs into practical AI prototypes and MVPs for internal teams and client-facing solutions. This role is intended for an early-career engineer who combines strong software engineering foundations with curiosity for insurance and business processes, appetite for hands-on exposure to modern AI-native ecosystems, and commitment to building solutions that demonstrate value quickly. Working under the guidance of domain-knowledge business experts, applied AI consultants, senior AI engineers, and senior AI architects, the AI Engineer will rapidly explore use cases, design solution approaches, build working proof-of-concepts, and validate their business value with stakeholders. While the AI Engineer may contribute to developing scalable technical foundations and support the transition toward production, the scaling, industrialization, and long-term operation of solutions will primarily be led by the Technology team. Key Responsibilities: Business Problem & Workflow Translation - Work with domain-knowledge business experts, applied AI consultants, product owners, and stakeholders to understand operational workflows, expert-driven reasoning, business problems, and user needs. - Identify where AI can augment expert judgment, automate repetitive tasks, improve access to knowledge, or accelerate analytical workflows. AI Prototype & MVP Development - Design and build working AI prototypes, proof-of-concepts, and MVPs that demonstrate practical business value quickly. - Develop prototype code bases, lightweight applications, APIs, articulating prompts, retrieval workflows, agents, and their evaluation scripts to test AI-enabled use cases. - Prepare and structure data, documents, knowledge sources, evaluation datasets, and workflow logic needed to validate prototypes. Stakeholder Validation & Iteration - Validate prototypes with business stakeholders, domain experts, and end users to assess usability, relevance, accuracy, limitations, and potential business impact. - Iterate rapidly based on feedback, testing outcomes, observed user behavior, and evolving understanding of the workflow. - Document prototype assumptions, design decisions, evaluation results, known limitations, and recommended next steps in a way that is accessible to both technical and non-technical audiences. Technology Handoff - Support the transition of prototypes toward scalable solutions by preparing clear handoff materials for the Technology team, including architecture notes, dependencies, risks, security considerations, and production-readiness gaps. - Apply best AI development principles throughout prototyping, including privacy, security, transparency, human oversight, quality evaluation, and appropriate use of AI outputs. Expected Technical Stack - Programming: Fluency in Python, understanding of .Net; familiarity with SQL and basic software engineering practices. - AI/ML frameworks: familiarity with PyTorch, TensorFlow, scikit-learn, NumPy, pandas, MLflow or other experiment tracking and model lifecycle management frameworks, experience with tooling for developing reproducible data and ML pipelines, and related data science libraries. - Generative AI: LLM APIs, prompt engineering, embeddings, vector databases, retrieval-augmented generation, evaluation methods, and agentic workflow concepts. - Application development: REST APIs, FastAPI or similar frameworks, Git, testing frameworks, and basic front-end and application integration concepts. - Cloud & deployment: Microsoft Azure preferred; exposure to GCP, Databricks and AWS is a plus. Familiarity with Docker, CI/CD, monitoring, and secure deployment practices. Qualifications: - Bachelor’s or Master’s degree in Computer Science, Engineering, Applied Mathematics, Data Science, Artificial Intelligence, or a related quantitative field. - 1–3 years of relevant experience, which may include internships, academic or coursework-related projects, personal AI projects, open-source contributions, or early professional experience in AI, machine learning, software engineering, or data engineering. - Exposure to insurance, actuarial science, or financial services is a plus but not required. - Ability to understand business workflows and translate them into practical AI solution designs. - Strong communication skills, with the ability to engage non-technical stakeholders and clarify ambiguous requirements. - Curiosity, problem-solving, and collaborative mindset.