Note: The job is a remote job and is open to candidates in USA. Milliman is a global consulting firm that has established a new practice called Milliman AI Solutions to enhance operational efficiency through artificial intelligence. They are seeking an AI Engineer to develop AI prototypes and MVPs that address business problems and improve workflows, collaborating with various stakeholders to validate and iterate on these solutions.
Responsibilities
- 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
- 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
- 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
- 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
Skills
- 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
- 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
- Exposure to insurance, actuarial science, or financial services is a plus but not required
Company Overview
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