Principal Software Engineer – GRS Innovation (Generative AI)

Liberty Mutual•Boston, MA

2d•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 works with Engineering, Architecture, Data, Analytics, and business partners to identify where generative AI can provide differentiated value, including work on an enterprise AI context plane and intelligent document processing capabilities. The Principal Software Engineer will be a hands-on engineer working across experimentation and practical delivery. The role involves exploring ambiguous problem spaces, assessing and testing emerging technologies, and creating credible paths from prototypes to production. The engineer will shape technical direction across multiple initiatives, influence architecture beyond the immediate team, and increase engineering effectiveness through reusable approaches. The position follows a hybrid work model with two days onsite and is open to candidates located in Portsmouth, New Hampshire; Boston, Massachusetts; Plano, Texas; Indianapolis, Indiana; or Columbus, Ohio. 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.
Experience with generative AI techniques and architectures such as retrieval-augmented generation, agents and tool use, prompt and context engineering, model integration, and structured-output generation.
Experience evaluating generative AI 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 or JavaScript.
Experience with widely adopted AI frameworks, model APIs, vector search technologies, orchestration toolkits, or similar components of the generative AI 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 well defined.
Strong written and verbal communication skills, including the ability to present technical work and recommendations to senior leadership.
A Bachelor’s or Master’s degree in a technical or business discipline, or equivalent experience.
Must be authorized 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 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 assess an idea’s 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 architecture, automated testing, deployment guidance, technical documentation, and knowledge transfer.
Account for security, responsible AI, observability, resiliency, cost, and operational ownership when designing solutions.
Partner with application teams to help experiments evolve into maintainable, resilient, and secure production solutions.
Work with architects, technical leads, product owners, and business leaders to clarify needs, identify assumptions, and break loosely defined opportunities into testable increments.
Translate business problems into software and architecture decisions, while balancing experimentation and customer value with enterprise engineering standards and long-term technology direction.
Communicate with customers, teammates, and leadership to determine whether experiments should advance, change direction, or stop.
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.
Mentor engineers and peers 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.
At the Principal level, shape technical direction across multiple initiatives, influence architecture beyond the immediate team, and increase the effectiveness of engineers and teams through reusable approaches.