What is an AI Engineer at AIG?

At AIG, the role of an AI Engineer—specifically within our Early Career Gen AI Technology and Data Engineering tracks—is pivotal to our transformation. We are not just maintaining legacy systems; we are actively reimagining how a global leader in risk management operates. In this position, you will join a dedicated team in Atlanta, GA, focused on leveraging Generative AI to modernize claims processing, underwriting, and risk assessment.

You will work at the intersection of software engineering and data science. Whether you are building core frameworks using Java Spring Boot and Angular, or designing cloud-native big data solutions with Spark and AWS SageMaker, your work will directly impact how we serve clients in over 190 countries. This role is designed for innovators who want to apply cutting-edge technology—like Large Language Models (LLMs) and automated testing frameworks—to solve complex, real-world enterprise challenges.

This is more than a coding job; it is a strategic entry point into the financial services industry. As part of our cohort-based program starting in July 2026, you will receive mentorship from senior architects and gain exposure to enterprise-scale tools like Palantir Foundry, Snowflake, and Splunk. You will be responsible for building the digital workflows and intelligent applications that will define the future of insurance.

Common Interview Questions

Getting Ready for Your Interviews

Preparation for AIG is about demonstrating a balance of technical academic excellence and a collaborative, professional mindset. Because this is an early career program, we look for potential and foundational strength rather than decades of experience.

Technical Foundation & Gen AI Curiosity – You must demonstrate a strong grasp of core engineering principles (Data Structures, Algorithms, OOP) and a specific interest in AI. We evaluate how well you understand the modern data stack (e.g., Cloud, APIs, ETL) and your familiarity with Generative AI concepts. You don't need to be a research scientist, but you must understand how to apply AI tools to build software.

Analytical Problem Solving – Insurance is a data-driven industry. We assess your ability to take a vague business requirement—such as "improve claim sorting"—and translate it into a technical solution using data pipelines or automated workflows. We look for candidates who can articulate their thought process clearly.

Collaboration & Communication – AIG values "connected cohorts." You will likely face behavioral questions that test your ability to work in teams, handle feedback, and communicate technical concepts to non-technical stakeholders. We are looking for future leaders who can navigate a large, global organization.

Academic & Professional Discipline – We have a strict requirement for academic performance (minimum 3.4 GPA). During the interview, we will look for evidence of this discipline in how you structure your code, how you document your projects, and how you prepare for the interview itself.

Interview Process Overview

The interview process for the Early Career AI and Engineering Analyst program is structured to be rigorous yet educational. It typically moves from an initial screening to a more comprehensive assessment of your technical and behavioral fit. Because we hire in cohorts, the process is standardized to ensure fairness and to identify candidates who will thrive in our collaborative culture.

You should expect an initial digital assessment or HireVue interview, which focuses on behavioral questions and basic technical concepts. This is often followed by a technical screening or online coding challenge that tests your proficiency in languages like Java, Python, or SQL. Successful candidates are then invited to a final round (often a "Super Day" or panel format), where you will interact with senior engineers and hiring managers. This final stage delves deeper into your resume, your understanding of Gen AI, and your alignment with AIG's values.

The timeline above illustrates the typical flow from application to offer. Note that the "Final Round" often combines technical case studies with behavioral interviews. Use the time between stages to brush up on the specific technologies mentioned in the job description, such as Spring Boot or AWS services, as these are fair game for discussion.

Deep Dive into Evaluation Areas

We evaluate candidates on a mix of modern software engineering skills and specific data/AI competencies. Based on our job descriptions, you should focus your preparation on the following areas.

Generative AI & Data Engineering

This is the core of the role. You need to understand how data moves through an enterprise and how AI models are integrated into applications. We are looking for candidates who understand the "plumbing" of AI—not just the theory.

Be ready to go over:

• Cloud-Native Data Tools – Familiarity with the Hadoop ecosystem, Spark (PySpark/Spark SQL), and cloud platforms like AWS.
• Gen AI Integration – How to use APIs to integrate LLMs into business applications.
• Model Operationalization – Concepts around MLOps, deploying models using AWS SageMaker, or using platforms like Palantir Foundry.
• Advanced concepts – Knowledge of Vector Databases, RAG (Retrieval-Augmented Generation), or handling hallucinations in Gen AI models.

Example questions or scenarios:

• "How would you design a data pipeline to ingest real-time claims data for an AI model?"
• "Explain the difference between a traditional database and a data warehouse like Snowflake."
• "Describe a project where you used an API to connect a front-end application to a back-end service."

Software Development Fundamentals

Whether you are in the Data or Software track, you must be a competent coder. We rely heavily on Java and modern web frameworks.

Be ready to go over:

• Backend Development – Java Spring Boot is critical at AIG. Understand dependency injection, RESTful API design, and microservices architecture.
• Frontend Basics – Understanding Angular or general JavaScript/TypeScript frameworks to build user interfaces.
• Quality Assurance – Knowledge of automated testing frameworks and how to use Gen AI to assist in writing tests.

Example questions or scenarios:

• "Walk me through how you would create a REST API endpoint in Java Spring Boot."
• "How do you handle version control and merge conflicts in a team environment?"
• "What is the role of an API Gateway in a microservices architecture?"

Behavioral & Operational Excellence

We operate in a regulated, high-stakes industry. Your ability to work responsibly and effectively is just as important as your code.

Be ready to go over:

• Observability – Using tools like Splunk to monitor application health and debug issues.
• Release Management – Understanding CI/CD pipelines and how to deploy software safely.
• Culture Fit – Why you want to work in Insurance, and how you demonstrate AIG's values of diversity and inclusion.

Example questions or scenarios:

• "Tell me about a time you had to learn a new technology quickly to finish a project."
• "Describe a situation where you identified a bug in production. How did you troubleshoot and resolve it?"
• "Why are you interested in starting your career at AIG specifically?"