Artificial Intelligence (AI) Engineer

Milliman • Dallas, TX

17h • $78,800 - $145,130 • Remote

About The Position

Milliman 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.

Requirements

• 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.
• Fluency in Python, understanding of .Net; familiarity with SQL and basic software engineering practices.
• 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.
• Familiarity with LLM APIs, prompt engineering, embeddings, vector databases, retrieval-augmented generation, evaluation methods, and agentic workflow concepts.
• Familiarity with REST APIs, FastAPI or similar frameworks, Git, testing frameworks, and basic front-end and application integration concepts.
• Microsoft Azure preferred; exposure to GCP, Databricks and AWS is a plus. Familiarity with Docker, CI/CD, monitoring, and secure deployment practices.

Nice To Haves

• Exposure to insurance, actuarial science, or financial services is a plus but not required.

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.

Benefits

• The overall range for this role is $78,800 - $145,130.
• For candidates residing in: Alaska, California, Connecticut, Illinois, Maryland, Massachusetts, New Jersey, New York City, Pennsylvania, Virginia, Washington, or the District of Columbia the range is $90,620 - $145,130.
• All other locations the range is $78,800 - $126,200.

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level