Principal AI Engineer

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You have a clear vision of where your career can go. And we have the leadership to help you get there. At CNA, we strive to create a culture in which people know they matter and are part of something important, ensuring the abilities of all employees are used to their fullest potential. Individual contributor providing the highest level of leadership in AI-native engineering, agentic system design, and applied AI solution development across CNA's Underwriting and Operations portfolio. This position has a deep understanding of AI engineering platforms, large language model (LLM) tooling, and enterprise software delivery from an end-to-end perspective. The focus of this position is designing and scaling AI-enabled solutions that integrate into underwriting workflows, drive measurable business impact, and establish durable AI engineering practices across the organization. JOB DESCRIPTION: Essential Duties & Responsibilities Performs a combination of duties in accordance with departmental 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 be leveraged across 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 perform additional duties as assigned. Reporting Relationship Typically Director 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 considerations impacting AI in insurance, including auditability, transparency, and appropriate use of AI in decisioning. Education & Experience Bachelor's degree in Computer Science , Engineering, or a related field required ; Master's degree preferred. Typically a 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. Experi

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