Principal Software Engineer – GRS Innovation (Generative AI)

Liberty Mutual Insurance•Boston, MA

3d•$120,000 - $225,000•Hybrid

About The Position

Liberty Mutual’s Global Risk Solutions (GRS) Innovation team explores emerging technologies and turns promising ideas into proofs of concept, initial pilots, and reusable engineering patterns for commercial and specialty insurance. The team partners with Engineering, Architecture, Data, Analytics, and business teams to identify where emerging capabilities—particularly generative AI—can provide differentiated value. Current work includes an enterprise AI context plane and intelligent document processing capabilities. The Principal Software Engineer will work at the intersection of experimentation and practical delivery: exploring ambiguous problem spaces, testing new technical possibilities, and creating credible paths from prototype to production. The role designs and validates GenAI solutions, builds reusable implementations and components, and helps application teams productionize solutions with attention to security, responsible AI, observability, resiliency, cost, and operations. At the Principal level, the engineer also shapes technical direction across initiatives, influences architecture beyond the immediate team, and increases engineering effectiveness through broadly reusable approaches. This is a hybrid position requiring two days onsite each week. It is open to candidates located in Portsmouth, NH; Boston, MA; Plano, TX; Indianapolis, IN; and Columbus, OH. Applicants must be authorized to work in the United States without current or future employer sponsorship.

Requirements

At least 8 years of software engineering experience.
Hands-on experience designing and building generative AI applications and capabilities to solve real problems.
Experience with 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 GenAI ecosystem components.
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 well defined.
Strong written and verbal communication skills, including presenting technical work and recommendations to senior leadership.
Bachelor’s or Master’s degree in a technical or business discipline, or equivalent experience.
Authorization to work in the United States without employer sponsorship now or in the future.

Nice To Haves

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.

Responsibilities

Design and build proofs of concept and initial pilots applying generative AI to commercial and specialty insurance problems.
Develop capabilities for intelligent document processing, extraction, summarization, question answering, conversational experiences, retrieval-augmented generation, and AI agents.
Frame hypotheses, define success measures, and conduct disciplined experiments to assess value, feasibility, security, and scalability.
Evaluate emerging models, frameworks, and architectural patterns based on evidence.
Build high-quality reference implementations, reusable components, APIs, integrations, user interfaces, and automation that application teams can adopt and productionize.
Establish transition paths from prototypes to production 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.
Partner with application teams to evolve experiments into maintainable, resilient, and secure production solutions with minimal unnecessary rework.
Work with architects, technical leads, product owners, and business leaders to clarify needs, surface assumptions, and break ambiguous opportunities into testable iterative increments.
Translate business problems into software and architecture decisions while staying current on emerging technologies relevant to Liberty Mutual.
Balance rapid learning and customer value with enterprise engineering standards and long-term technology direction.
Determine whether experiments should advance, change direction, or stop, based on communication with customers, the team, and leadership.
Build trusted relationships with engineers, architects, data professionals, business partners, and enterprise AI teams.
Communicate technical concepts and recommendations through documentation, posts, architecture diagrams, meetings, and other channels to technical and business audiences, including senior leaders.
Mentor teammates and peer engineers through pairing, design reviews, technical guidance, demonstrations, and training.
Support GRS domain teams conducting innovation experiments or implementations.
Share reusable patterns, lessons, and standards that improve how teams build and evaluate AI-enabled solutions.
Shape technical direction across multiple initiatives and influence architecture beyond the immediate team.
Improve the effectiveness of engineers and teams through broadly reusable approaches.