Principal Software Engineer – GRS Innovation (Generative AI)

Build and validate emerging solutions Design and build proofs of concept and initial pilots that apply generative AI to real commercial and specialty insurance problems. Develop capabilities involving intelligent document processing, extraction, summarization, question answering, conversational experiences, retrieval-augmented generation, and AI agents. Frame hypotheses, define success measures, and use disciplined experimentation to determine whether an idea is valuable, feasible, secure, and scalable. Evaluate emerging models, frameworks, and architectural patterns based on evidence rather than novelty. Build high-quality reference implementations, reusable components, APIs, integrations, UIs, and automation that application teams can adopt and productionize. Establish clear transition paths through sound architecture, automated testing, deployment guidance, technical documentation, and knowledge transfer. Design solutions with security, responsible AI, observability, resiliency, cost, and operational ownership in mind—even when the initial deliverable is a proof of concept. Build trusted relationships with aligned team, peer engineers, architects, data professionals, business partners, and enterprise AI teams. Support GRS domain teams attempting their own innovation experiments or implementations. Hands-on experience designing and building generative AI applications. We're looking for engineers who can build GenAI capabilities and best practices to solve real problems — going beyond engineers who use AI-assisted development tools well. Experience with relevant GenAI techniques and architectures, such as retrieval-augmented generation, agents and tool use, prompt and context engineering, model integration, and structured-output generation. Experience evaluating GenAI solutions for quality, reliability, safety, and business usefulness using repeatable testing and evaluation practices. Strong programming skills in modern languages used for AI and application development, particularly Python and TypeScript/JavaScript. Experience with widely adopted AI frameworks, model APIs, vector search technologies, orchestration toolkits, or similar components of the GenAI ecosystem. Experience designing APIs, integrations, automation, and reusable software components. Experience developing cloud-native applications on AWS or a comparable cloud platform. Experience with automated testing, source control, CI/CD pipelines, and modern software-delivery practices. Ability to independently structure and advance work when requirements, solution boundaries, or technical approaches are not yet well defined. Strong written and verbal communication skills, including the ability to present technical work and recommendations to senior leadership. Experience with intelligent document processing, document understanding, multimodal AI, or high-volume unstructured data. Experience designing or implementing enterprise context-management, knowledge-retrieval, or data-grounding capabilities for AI applications. Familiarity with responsible-AI practices, including data protection, model risk, guardrails, evaluation, traceability, and human oversight. Experience mentoring engineers, leading technical communities, or creating technical training and enablement. Experience applying emerging technologies in large, regulated enterprises. Knowledge of commercial or specialty insurance, particularly complex and high-touch underwriting, claims, or servicing workflows. Experience with relevant GenAI techniques and architectures, such as retrieval-augmented generation, agents and tool use, prompt and context engineering, model integration, and structured-output generation.Experience evaluating GenAI solutions for quality, reliability, safety, and business usefulness using repeatable testing and evaluation practices. A Bachelor's or Master's degree in a technical or business discipline, or equivalent experience