Principal AI Engineer

Based in Chicago with a hybrid work arrangement, CNA Insurance is advancing its AI-enabled software strategy by building an enterprise-grade AI-native engineering platform. The Principal AI Engineer serves as the senior technical authority shaping this platform, spanning agentic coding workflows, skills marketplaces, AI-augmented CI/CD pipelines, and governance guardrails designed to scale across hundreds of engineers while preserving security and architectural integrity. This role blends strategic leadership with hands-on technical stewardship to help the organization design, ship, and operate high-quality AI-native systems at AI speed.

Responsibilities

• Act as a principal engineer for CNA's AI-native engineering platform, crafting the end-to-end system that includes agentic coding workflows, skills and agent marketplaces, AI-augmented CI/CD pipelines, automated quality gates, and rapid environment provisioning. Drive 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 engineering teams to move at AI-native speed without compromising architecture or security.
• Provide expert technical guidance to engineering leadership, portfolio teams, and architecture on adopting AI-native development practices, evaluating AI-generated code quality, and embedding agentic tooling into existing workflows. Advise on trade-offs between speed and quality, human-in-the-loop requirements, and appropriate AI autonomy for different risk profiles.
• Lead the technical strategy and implementation of the engineering metrics platform, collaborating with senior technology leaders to enable data-driven decision making.
• Mentor engineers across the organization in AI-native engineering practices, including agentic coding, context engineering, prompt-to-code workflows, and AI-assisted testing, elevating the team’s capabilities toward self-sufficiency.
• Research, evaluate, and recommend AI engineering tools, frameworks, and infrastructure aligned with CNA’s strategic direction; lead build-versus-buy analyses for platform capabilities such as CI/CD tooling, sandbox provisioning, and LLM evaluation infrastructure.
• Partner with Architecture, Security, Cloud Engineering, and Data teams to ensure integration with enterprise infrastructure (GCP/GKE, GitHub, JFrog Artifactory), meet regulatory and compliance requirements (AI model tracking, SOX controls), and scale to support hundreds of engineers and AI pod teams across portfolios.
• May perform additional duties as assigned.

Requirements

• Expert knowledge of 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 phase, from requirements and design through AI-assisted coding, 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 causing delivery bottlenecks.
• Strong knowledge of application security engineering, including supply chain security, artifact management, static/dynamic analysis, secret management, and AI-generated code attack vectors (dependency hallucination, model drift, prompt injection).
• Proven track record designing developer platforms and tooling that serve hundreds of engineers with guardrails to maintain code quality at scale.
• Demonstrated ability to evaluate and integrate emerging AI technologies rapidly, with sound judgment to distinguish hype from production-ready capability in a fast-changing tooling landscape.
• Excellent communication skills, with the ability to translate complex AI concepts for technical and non-technical audiences and influence engineering culture across a large organization and its service providers.
• Strong analytical and problem-solving skills with an outcomes-oriented mindset focused on tangible improvements in delivery speed, code quality, and engineering productivity.
• Education: Bachelor's Degree required; Master's preferred. Experience: minimum of 9 years.

Technologies

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

Benefits

Comprehensive and competitive benefits package.

Salary

USD 97,000 – 189,000 per year.

Reporting relationship

Typically reports to a Director or above.