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Essential Duties & Responsibilities

Performs a combination of dutiesin accordance withdepartmental guidelines:

• Leads the scaling of AI solutions within the Underwriting portfolio, ensuring AI capabilities developed in product pods are industrialized into reliable, reusable, enterprise-grade services that integrate seamlessly into underwriting and operations workflows.

• Partners closely with underwriting, operations, and product teams to translate domain-specific workflows into AI-enabled solutions - including submission intake, risk triage, data enrichment, pricing signals, and decision support - ensuring alignment to real-world underwriting processes and measurable business impact.

• Designs AI integration patterns across core underwriting and operations systems - including underwriting workbench platforms and document processing pipelines - ensuring solutions are performant, scalable, and embedded directly into decisioning workflows.

• Acts as a senior technical mentor, developing engineers across the organization in AI-native practices including agentic coding patterns, context engineering, prompt-to-code workflows, and AI-assisted testing. Builds durable, self-sustaining team capability without ongoing coaching dependency.

• Establishes patterns for AI governance, explainability, and auditability in underwriting use cases, ensuring AI-driven decisions meet regulatory, compliance, and internal risk management expectations.

• Drives reusability and cross-business-unit scalability of AI solutions, designing capabilities that can beleveragedacross underwriting segments while accounting for differences in data, workflows, and risk profiles.

• Researches, evaluates, and recommends AI engineering tools, frameworks, and infrastructure (e.g., eval platforms, agent orchestration systems, environment provisioning automation), supporting build-vs-buy decisions with a focus on long-term scalability and maintainability.

May performadditionalduties as assigned.

Reporting Relationship

TypicallyDirector or above

Skills, Knowledge & Abilities

• Expert knowledge of AI-native engineering practices, including agentic system design, LLM integration, multi-agent orchestration, and context engineering.

• Deep understanding of enterprise software delivery, including CI/CD pipelines, automated quality gates, cloud-native architecture, and production-grade system design.

• Strong ability to translate complex business workflows - particularly in commercial insurance underwriting - into reliable, scalable AI-enabled solutions.

• Demonstrated ability to advise on AI governance, model explainability, and regulatory compliance considerations in high-stakes or regulated environments.

• Excellent analytical and problem-solving skills with the ability to evaluate build-vs-buy trade-offs and make sound architectural recommendations.

• Proven ability to mentor and develop engineering talent, raising organizational capability in emerging AI practices.

• Excellent communication and interpersonal skills with the ability to engage effectively with peers, technical leadership, and non-technical business stakeholders.

• Working knowledge of the commercial insurance underwriting lifecycle, including submission intake, risk triage and appetite matching, underwriting analysis, and quote-to-bind processes.

• Ability to translate underwriting judgment and business rules into AI-driven insights, model features, decision frameworks, and explainable outputs that can be trusted and adopted by underwriters.

• Familiarity with regulatory and governance considerationsimpactingAI in insurance, including auditability, transparency, andappropriate useof AI in decisioning.

Education & Experience

• Bachelor's degree in Computer Science, Engineering, or a related fieldrequired;Master'sdegree preferred.

• Typicallya minimum of 10 years of software engineering experience, with at least 3 years in a principal, staff, or architect-level role.

• Demonstrated experience designing and deploying agentic AI systems or AI-native developer platforms in production environments.

• Experience in commercial insurance or financial services, with familiarity in underwriting lifecycle processes, preferred.

• Applicable certifications in AI, cloud, or related disciplines preferred.

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