Principal AI Engineer

CNA Insurance in Chicago is seeking a Principal AI Engineer to lead the design and deployment of an AI-native engineering platform that supports agentic workflows, AI-enhanced CI/CD, reusable skills, and governance at enterprise scale. The role centers on enabling CNA’s broad engineering organization to build, ship, and operate high-quality, secure AI-native systems at the speed of AI, with strong guardrails and security integrated from the start. This hybrid, senior leadership position expects deep technical expertise, cross-team influence, and a focus on measurable engineering outcomes. The role offers a salary range of USD 97,000 to 189,000 per year and requires a minimum of 9 years of experience, a Bachelor's degree (Master's preferred).

Location & compensation — Chicago, IL (hybrid). Salary: USD 97,000 - 189,000 annually. Minimum experience: 9 years. Education: Bachelor's degree required; Master's preferred.

Responsibilities

• Serve as a principal engineer for CNA's AI-native engineering platform, shaping the end-to-end system that covers agentic coding workflows, skills and agent marketplaces, AI-augmented CI/CD pipelines, automated quality gates, and rapid environment provisioning. Lead the integration of AI tooling such as Claude Code, Cursor, and GitHub Copilot into the software delivery lifecycle to form a cohesive, governed platform.
• Design and build the agentic infrastructure layer, including multi-agent orchestration patterns, sub-agent frameworks, skill authoring standards, and context engineering best practices, enabling teams to move at AI-native speed while preserving architecture and security posture.
• Provide expert technical consultation to engineering leadership and portfolio teams on adopting AI-native development practices, evaluating AI-generated code quality, and weaving agentic tooling into existing workflows. Advise on speed-versus-quality trade-offs, human-in-the-loop requirements, and appropriate AI autonomy for different risk profiles.
• Lead the technical strategy and implementation for the engineering metrics platform, collaborating with senior technology leaders to drive data-driven decision making.
• Mentor engineers across the organization in AI-native engineering practices, including agentic coding patterns, context engineering, prompt-to-code workflows, and AI-assisted testing, raising capability floors so teams become self-sustaining.
• Research, evaluate, and recommend AI engineering tools, frameworks, and infrastructure aligned with CNA’s strategy. Lead build-vs-buy analyses for platform capabilities such as CI/CD tooling, sandbox provisioning, and LLM evaluation infrastructure.
• Collaborate closely with Architecture, Security, Cloud Engineering, and Data teams to ensure the AI engineering platform integrates with enterprise infrastructure (GCP/GKE, GitHub, JFrog Artifactory), meets regulatory and compliance requirements (AI model tracking, Sox controls), and scales to hundreds of engineers and AI pod teams across portfolios.
• Perform additional duties as assigned.

Requirements

• Expert knowledge in AI-native software engineering practices, including agentic coding workflows (Claude Code, Cursor, GitHub Copilot), prompt and context engineering, multi-agent orchestration, MCP protocol, and skill/agent authoring patterns.
• Deep understanding of the modern software delivery lifecycle and how AI transforms each stage, from requirements and design through agentic code generation, automated testing, AI-augmented code review, and continuous deployment.
• Proficiency in building and operating CI/CD platforms (GitHub 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 bottlenecks.
• Strong knowledge of application security engineering including supply chain security, artifact management, static/dynamic analysis, secret management, and AI-generated code risks (dependency hallucination, model drift, prompt injection).
• Proven ability to design developer platforms and tooling that serve hundreds of engineers at varying skill levels while balancing power-user capabilities with guardrails to maintain code quality at scale.
• Demonstrated ability to evaluate and rapidly integrate emerging AI technologies, with sound judgment to distinguish hype from production-ready capabilities in a fast-moving tooling landscape.
• Excellent communication skills with the ability to translate complex AI engineering concepts for technical and non-technical audiences, influencing culture and adoption across large teams and external providers.
• Strong analytical and problem-solving skills with an outcomes-oriented mindset focused on improving delivery speed, code quality, and engineering productivity.

Technologies

• Claude Code
• Cursor
• GitHub Copilot
• GitHub Actions
• Terraform
• GKE
• Kubernetes
• GCP
• JFrog Artifactory
• MCP protocol

Benefits

Comprehensive and competitive benefits package

Reporting relationship

Typically Director or above