AI Engineer at Milliman in Gurgaon

Job description

• The 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, with teams in the US, India and France. 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.
• Milliman AI Solutions is seeking an AI Engineer based in India to support remotely the New York Property & Casualty (P&C) insurance practice’s AI transformation initiatives. The role will focus on translating consulting workflows, business problems, large bodies of structured and unstructured data, expert reasoning, and user needs into practical AI prototypes and solutions for internal teams to leverage in client consulting work.
• This role is intended for an early-career engineer with a Computer Science and/or Engineering degree from a reputable institute, strong analytical and software engineering foundations, practical familiarity with Excel and R, and demonstrated ability to work effectively with large and complex datasets.
• The position is an engineering, data, and AI-enabled workflow transformation role for the P&C consulting practice. It is outside the actuarial track and does not require actuarial training or actuarial-track experience.

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.
• Use AI literacy and hands-on AI proficiency to identify opportunities to transform, simplify, automate, or improve project workflows, particularly within New York P&C engagements.
• AI 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.
• Work with large bodies of structured and unstructured data, including cleaning, organizing, analyzing, reconciling, and preparing data for prototype development, evaluation, and stakeholder validation.
• Leverage firmwide approved AI tooling and apply best AI development principles throughout prototyping, including privacy, security, transparency, human oversight, quality evaluation, and appropriate use of AI outputs.
• 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.
• Coordinate with New York-based P&C stakeholders and maintain flexibility to overlap with U.S. East Coast working hours at least part of the time, depending on project needs.

Requirements

• Programming and analysis: Fluency in Python; strong working knowledge of Excel and R; familiarity with SQL, basic software engineering practices.
• Data engineering and analytics: Experience developing and maintaining data pipelines; integrating, reconciling and validating large and complex datasets; automating data preparation workflows, and creating reproducible analytical datasets for modeling and reporting.
• 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.
• AI/ML frameworks: experience with tooling for developing reproducible data and ML pipelines, and related data science libraries - familiarity with PyTorch, TensorFlow, scikit-learn, NumPy, pandas, polars, MLflow or other experiment tracking and model lifecycle management frameworks is a plus.
• Cloud & deployment: Microsoft Azure preferred; exposure to GCP, Databricks and AWS is a plus. Familiarity with Docker, CI/CD, monitoring, and secure deployment practices.
• Bachelor’s or Master’s degree in Computer Science and/or Engineering from a reputable institute; equivalent technical training may be considered where supported by strong software, data, and AI project experience.
• 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.
• AI literate and practically proficient with modern AI tools, with the ability to apply them responsibly to improve consulting workflows, data preparation, analysis, documentation, stakeholder interaction, and delivery efficiency.
• Strong communication skills, with the ability to engage non-technical stakeholders and clarify ambiguous requirements.
• Curiosity, problem-solving, and collaborative mindset.

Skills

Required

• Python
• Excel
• R
• SQL
• Basic software engineering practices
• Data pipelines
• Automating data preparation workflows
• LLM APIs
• Prompt engineering
• Embeddings
• Vector databases
• Retrieval-augmented generation

Preferred

• REST APIs
• FastAPI or similar frameworks
• Git
• Testing frameworks
• PyTorch
• TensorFlow
• scikit-learn
• NumPy