An AI Engineer at Chubb plays a pivotal role in driving the digital transformation of one of the world's largest publicly traded property and casualty insurance companies. In this role, you are responsible for designing, building, and deploying enterprise-grade artificial intelligence systems that directly impact risk assessment, claims processing, and underwriting efficiency. By leveraging cutting-edge machine learning and natural language processing techniques, you help turn massive volumes of unstructured insurance data into actionable, automated decisions.

The impact of this position is felt across the entire organization. From developing agentic workflows that assist underwriters to building robust retrieval-augmented generation (RAG) systems for policy analysis, your work directly influences Chubb's operational speed and accuracy. You will work on productionizing Generative AI models, fine-tuning large language models (LLMs), and implementing scalable APIs that serve millions of requests, ensuring that AI solutions are not just experimental prototypes but robust, enterprise-ready systems.

This role requires a unique blend of deep theoretical knowledge in modern AI architectures and practical software engineering discipline. You will collaborate closely with data scientists, software engineers, and business leaders to integrate advanced AI capabilities into Chubb's core products. It is a highly strategic and challenging position where your technical contributions directly shape the future of risk management and insurance technology.

The following questions are representative of what you can expect during the Chubb hiring process. These questions are drawn from real interview experiences of candidates who have gone through the loop for the AI Engineer role. Use these to identify patterns in how the team evaluates technical depth and system-building capability.

Generative AI & Large Language Models

These questions assess your foundational understanding of modern generative models, their architectures, and how to optimize them for specific enterprise tasks.

• What are the core components of the Transformer architecture, and how does the self-attention mechanism work?
• Can you explain the process of RLHF (Reinforcement Learning from Human Feedback) and how it differs from supervised fine-tuning?

To succeed in the Chubb AI Engineer interview process, you must demonstrate a balance of theoretical AI knowledge and practical software engineering execution. Your preparation should focus on showing that you can not only discuss advanced AI concepts but also write clean, production-grade code to implement them.

Role-Related Knowledge – You must possess a deep understanding of modern AI paradigms, particularly Transformers, LLMs, Fine-tuning, and AI Agents. Expect to be tested on the mathematical and conceptual foundations of these technologies, even in early stages of the interview process.

Engineering Rigor – Chubb values engineers who understand how to build for production. This means being highly proficient in Python, understanding API design with frameworks like FastAPI, and knowing how to containerize, deploy, and monitor models in a cloud environment.

Communication & Alignment – You need to be able to translate complex technical decisions into business value. Whether you are speaking with a recruiter, a peer engineer, or a business manager, your ability to articulate the "why" behind your technical choices is critical.

The interview process for the AI Engineer position at Chubb typically spans between 2 to 4 rounds, depending on the seniority of the role and the specific team. The process is designed to evaluate both your immediate technical capabilities and your long-term potential to build scalable systems within an enterprise framework.

You will start with an initial screening conversation, which is uniquely structured at Chubb. This is followed by technical deep dives that focus on your background, system design capabilities, and hands-on coding or architectural exercises. The process concludes with discussions around team fit, communication, and your experience deploying real-world AI systems.

The timeline above outlines the typical progression from the initial recruiter contact to the final decision. Candidates should use this timeline to pace their preparation, ensuring they are fully ready for deep technical discussions immediately following the initial screen. While the process can sometimes move quickly, expect a thorough evaluation at each stage of the loop.

Transformer Architectures & LLM Fundamentals

Understanding the inner workings of modern language models is highly critical for this role. Chubb relies on these architectures to process complex policy documents, automate claims, and extract key insights from unstructured text.

You should be prepared to discuss:

• Attention Mechanisms – The mathematical intuition behind scaled dot-product attention and multi-head attention.
• RLHF & Alignment – How reward models are trained and how PPO (Proximal Policy Optimization) is used to align model outputs.
• Model Quantization – Techniques like AWQ or GPTQ to reduce model size for cost-effective deployment.

Advanced concepts to master:

• Rotary Position Embeddings (RoPE) and their impact on context window scaling.
• KV Caching mechanisms and how they optimize autoregressive decoding speed.
• FlashAttention and its memory-efficiency benefits during training and inference.

Example scenarios:

• "Explain how you would implement a custom self-attention layer from scratch in PyTorch."
• "How does RLHF prevent a model from generating toxic or hallucinated outputs while preserving its helpfulness?"

Production Engineering & FastAPI

Building a model is only half the battle; deploying it securely and efficiently at scale is where engineering rigor comes in. You will be evaluated on your ability to build robust backend systems that interface with your AI models.

You should be prepared to discuss:

• Asynchronous API Design – Writing non-blocking code using async and await in Python.
• Concurrency & Scaling – Utilizing worker processes (e.g., Uvicorn/Gunicorn) and managing database or model connection pools.
• Containerization – Best practices for Dockerizing Python-based AI applications to ensure reproducible environments.

Advanced concepts to master:

• Batching strategies (e.g., dynamic batching) to maximize GPU utilization during concurrent API requests.
• Integrating ASGI servers with background message queues (like Celery or Redis) for long-running AI tasks.

Example scenarios:

• "Design a FastAPI endpoint that accepts a document, processes it through an LLM asynchronously, and returns a job ID to the client."
• "How do you handle dependency injection and middleware configuration in a production-grade FastAPI application?"

Prompt Engineering & Agentic Workflows

As Chubb builds more autonomous systems, your ability to design reliable, self-correcting agentic workflows becomes highly valuable. This area evaluates how you programmatically guide LLMs to perform multi-step reasoning tasks.

You should be prepared to discuss:

• Prompt Design – Structured prompting techniques such as Chain-of-Thought (CoT) and ReAct (Reasoning and Acting).
• Tool Use & Function Calling How to enable models to reliably call external database APIs or calculators.
• State Management – Keeping track of conversation history and agent state across complex, multi-turn interactions.

Advanced concepts to master:

• Multi-agent orchestration frameworks (e.g., LangGraph, AutoGen) and how to prevent infinite loops in agent execution.
• Evaluator-Optimizer loops to programmatically refine model outputs before presenting them to the user.

Example scenarios:

• "How would you design an AI Agent that can query an SQL database, format the results, and generate a PDF report for an underwriter?"
• "What strategies do you use when an LLM fails to output valid JSON during a critical function-calling step?"