Principal AI Engineer

Essential Duties & Responsibilities

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, GitHub 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, GitHub, 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

Skills, Knowledge & Abilities

• 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 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 (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 creating delivery bottlenecks.

• Strong knowledge of application security engineering including supply chain security, artifact management and curation, static/dynamic analysis, secret management, and the specific attack vectors introduced by AI-generated code (dependency hallucination, model drift, prompt injection).

• Demonstrated ability to design developer platforms and tooling that serve hundreds of engineers at varying skill levels — balancing power-user capability with guardrails that prevent misuse and maintain code quality at scale.

• Proven ability to evaluate and integrate emerging AI technologies rapidly, with the judgment to distinguish between hype and production-ready capability. Comfortable operating in a fast-moving domain where the tooling landscape changes weekly.

• Excellent communication skills with the ability to translate complex AI engineering concepts for both technical and non-technical audiences. Able to influence engineering culture and drive adoption of new practices across a large, diverse organization including internal teams and managed service providers.

• Strong analytical and problem-solving skills with an outcomes-oriented mindset — focused on measurable improvements in delivery speed, code quality, and engineering productivity rather than tooling adoption metrics.

Education & Experience

• Bachelor's Degree with Master's preferred in Computer Science, AI/ML, or related discipline, or equivalent work experience.

• Minimum of 9 years of solid, diverse work experience in software engineering with a minimum of 6 years in application development, including significant recent experience (2+ years) building or operating AI-augmented development tools, agentic systems, or developer platforms.

• Demonstrated hands-on experience with LLM-based engineering tools (Claude Code, Cursor, GitHub Copilot, or equivalent) in production engineering workflows, not just experimental use.

• Experience in designing and scaling inner-source or platform engineering programs across large engineering organizations preferred.

• Applicable certifications in cloud platforms (GCP, AWS), AI/ML, or security preferred.