Corporate Vice President - Head of Enterprise AI Platform

Location Designation: Hybrid - 3 days per week

Role Overview We are building a small, senior AI engineering team responsible for creating New York Life’s enterprise AI platform—and the first generation of agentic AI solutions that run on it.

This team sits at the intersection of AI platform engineering, agentic system design, cloud architecture, and enterprise governance. Our mission is to build secure, governable, production-ready AI capabilities on top of enterprise data, APIs, and cloud-native platform services, moving beyond pilots to scalable business outcomes.

As AI Platform Lead, you will define how agentic AI is architected, deployed, secured, evaluated, and scaled responsibly across the enterprise. You will lead the team building the company’s AI backbone—the governed platform that powers enterprise AI applications and enables the first wave of agentic insurance solutions. This is a rare opportunity to shape a greenfield platform with executive sponsorship, modern cloud technologies, and a small, highly experienced engineering team.

This is a hands-on player-coach role for an engineering leader who enjoys deep technical ownership across the full technology stack—from cloud infrastructure and runtime architecture to tool orchestration, retrieval, memory, evaluation, and agent behavior. You will establish the technical vision, mentor senior engineers, and remain actively involved in architecture, coding, and solution delivery.

The platform is built on Google Cloud and designed to leverage cloud-native capabilities while maintaining portability through open architectures and reusable engineering patterns. Success in this role requires balancing innovation with governance, enabling responsible AI adoption in a highly regulated enterprise while delivering measurable business value.

Our Engineering Principles Our team is intentionally small, senior, and highly technical. Regardless of title, every engineer is expected to build AI systems—and build with AI.

• Build agents that power the platform. Develop platform capabilities as intelligent agents—not just traditional services. Examples include lifecycle management agents that register, version, monitor, govern, and retire AI assets across the enterprise.
• Build cloud agents that plan and implement. Create agents that can translate business needs into implementation plans, orchestrate the required skills and tooling, and execute work with human oversight at the appropriate checkpoints.
• Build end-to-end multi-agent solutions. Design and implement solutions where specialized agents collaborate to architect systems, provision infrastructure, generate code, validate through dedicated testing agents, deploy applications, perform post-deployment verification, and maintain complete operational traceability.
• Build with AI-assisted engineering tools. Be fluent with modern AI development tools such as Cursor, Claude Code, GitHub Copilot, Windsurf, or equivalent technologies. AI-assisted software development is a core engineering competency and will be evaluated throughout the interview process.

What You’ll Do

• Define the technical vision and reference architecture across the enterprise AI platform, including agent lifecycle services, orchestration capabilities, retrieval and memory services, and AI control plane components that integrate with enterprise AI services.
• Own and execute the multi-phase platform roadmap, delivering foundational AI platform capabilities, reusable platform services, enterprise knowledge capabilities, and end-to-end builder agents that accelerate AI solution delivery.
• Lead, mentor, and develop a high-performing team of senior AI engineers while maintaining hands-on ownership of architecture, engineering, and agent development. Establish a high engineering bar and champion AI-assisted software development practices.
• Partner with Security, Risk, Legal, and enterprise stakeholders to operationalize AI governance, automate solution review processes, and ensure alignment with enterprise AI risk management standards and responsible AI practices.
• Drive cloud architecture decisions, build-versus-buy evaluations, and platform portability strategies while leveraging Google Cloud capabilities without creating unnecessary vendor lock-in.
• Establish AI FinOps capabilities, including model usage visibility, infrastructure cost attribution, dashboards, and budget guardrails that enable r…