You’ll join The Auto Physical Damage (APD) squad within Liberty Mutual’s Claims Predictive Modeling domain. Our engineers are the bridge between data science and production: we partner directly with data scientists to take predictive and generative AI models out of the lab and operationalize them as real-time and batch services that drive automated claims decisions.

U.S. Based role only

Hybrid role: Requires two days in the office a week in Plano, Boston, Portsmouth, Indianapolis, or Columbus. 

Who you’ll work with

You’ll sit on a scrum squad alongside product owners, a solutions engineer, and fellow engineers — and you’ll work shoulder-to-shoulder with the claims data science team. Data science owns model development and prompt engineering; Our team owns the deployed environments, the integration architecture, and the production health of everything that ships. You’ll also partner with the teams that consume our output, including the adjuster-facing claims platforms and downstream subrogation and intake systems.

Responsibilities

• Model operationalization: Partner with data scientists to deploy models as production microservices, building the feature pipelines, orchestration, and integration layers that deliver predictions to downstream claims systems
• Integration engineering: Build and maintain event-driven and API-based integrations between predictive services and enterprise claims platforms, including Guidewire Navigator and FNOL intake
• GenAI enablement: Help operationalize generative AI use cases in the claims space — including prompt-driven services and the evaluation frameworks that measure whether LLM output is good enough to act on
• Production ownership: Own monitoring, alerting, and operational support for the services the squad runs, including on-call and service desk support for live model integrations
• Collaborative partner: Working with our team of scrum masters, product owners and fellow engineers, you’ll tackle technical challenges and ensure quality as we move from legacy technologies to next-generation applications
• Comprehensive problem-solver: As you manage the end-to-end development of software products, you’ll analyze issues at the system level and handle any complications that arise by implementing effective solutions
• Skilled technical engineer: You’ll document and lead the implementation of technical features, improvements and innovations
• Forward thinker: Simply fixing the problem isn’t enough; using your proactive mindset and initiative, you’ll continually look for ways to improve performance, quality and efficiency

• A minimum of five years of software engineering experience
• Hands-on experience with Java and Spring Boot for microservice development
• Experience building on AWS — Lambda, API Gateway, Step Functions, SQS, DynamoDB, S3, and RDS/MySQL — ideally with infrastructure as code (AWS CDK)
• Python for CDK
• Experience with event-driven and data pipeline technologies such as Kafka, Apache Airflow, and Snowflake
• Familiarity with MLOps concepts — model deployment, versioning, feature pipelines, and monitoring of model-serving services
• Experience with observability tooling (Datadog, Splunk) and CI/CD pipelines
• A history of translating client requirements into technical designs
• Agile engineering capabilities and a design-thinking mindset
• Collaboration, adaptability, flexibility and the ability to manage time and prioritize work with a globally distributed development team
• Strong oral and written communication skills — and a knack for explaining your decision-making process to non-engineers
• A thorough grasp of IT concepts, business operations, design and development tools, system architecture and technical standards, shared software concepts and layered solutions and designs
• An understanding of how modifications affect different parts of a system
• A background in business operations and strategies, with a focus on business IT
• A bachelor’s or master’s degree in a technical or business discipline, or equivalent experience

Nice to have, but not required

• • Exposure to LLM/GenAI application patterns — prompt engineering, retrieval, evaluation and quality metrics
  • Insurance claims domain knowledge
  • Databricks platform
  • Experience with computer vision or OCR pipelines