Principal AI Engineer

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.

Performs a combination of duties in accordance with departmental guidelines:

• Acts as one of principal engineers for CNA's AI-native engineering platform, designing the end-to-end system spanning agentic coding workflows, skills and agent marketplaces, AI-augmented CI/CD pipelines, automated quality gates, and rapid environment provisioning.
• Leads integration of AI tooling (Claude Code, Cursor, Git Hub Copilot) into the software delivery lifecycle, ensuring these capabilities compose into a coherent, governed platform.
• Designs and builds the agentic infrastructure layer — including multi-agent orchestration patterns, sub-agent frameworks, skill authoring standards, and context engineering best practices — that enables engineering teams to operate at AI-native speed without sacrificing architectural integrity or security posture.
• Provides expert technical consultation to engineering leadership, portfolio teams, and architecture on how to adopt AI-native development practices, evaluate AI-generated code quality, and integrate agentic tooling into existing workflows.
• Advises on trade-offs between speed and quality, human-in-the-loop requirements, and appropriate levels of AI autonomy for different risk profiles (e.g., Sox-classified systems vs. rapid prototyping).
• Leads the technical strategy and implementation for engineering metrics platform.
• Consult with senior tech leadership stakeholders to provide insights in data-driven decision making.
• Acts as the senior technical resource mentoring engineers across the organization in AI-native engineering practices — including agentic coding patterns, context engineering, prompt-to-code workflows, and AI-assisted testing — raising the floor of capability so teams become self-sustaining without ongoing coaching dependency.
• Researches, evaluates, and recommends AI engineering tools, frameworks, and infrastructure (e.g., eval platforms, agent orchestration systems, environment provisioning automation) aligned with CNA's strategic direction.
• Leads build-vs-buy analysis for platform capabilities such as CI/CD tooling, sandbox provisioning, and LLM evaluation infrastructure.
• Partners closely with Architecture, Security, Cloud Engineering, and Data teams to ensure the AI engineering platform integrates with enterprise infrastructure (GCP/GKE, Git Hub, JFrog Artifactory), meets regulatory and compliance requirements (AI model tracking, Sox controls), and scales to support hundreds of engineers and AI pod teams across all portfolios.
• May perform additional duties as assigned.

Reporting Relationship Typically Director or above.

• Expert knowledge in AI-native software engineering practices including agentic coding workflows (Claude Code, Cursor, Git Hub Copilot), prompt and context engineering, multi-agent orchestration, MCP protocol, and skill/agent authoring patterns.
• Deep understanding of the modern software delivery lifecycle with specific expertise in how AI transforms each phase — from AI-assisted requirements and design through agentic code generation, automated testing, AI-augmented code review, and continuous deployment.
• Proficient in building and operating CI/CD platforms (Git Hub Actions or equivalent), infrastructure-as-code (Terraform), container orchestration (GKE/Kubernetes), and cloud platforms (GCP), with the ability to design pipelines that enforce quality and security gates without creating delivery…