Corporate Vice President - Enterprise AI Architect

Location Designation: Hybrid - 3 days per week

Role Overview As the Enterprise AI Architect, you will serve as the technical anchor for the platform and the most senior individual contributor on the team and also AI architect for AIGC. You will define the engineering patterns, reference architectures, and reusable platform capabilities that every AI solution built across the organization will inherit.

This role is responsible for designing and building the platform’s most complex components—including AI lifecycle registries, control-plane services, harness and memory capabilities, and the engineering foundations that enable teams to rapidly develop, deploy, and govern agentic AI solutions. You’ll partner closely with the AI Platform Lead while providing technical mentorship across the engineering team through architecture reviews, engineering standards, and hands-on implementation.

This is a deeply technical role for an engineer who enjoys solving difficult distributed systems problems across cloud infrastructure, platform engineering, AI runtimes, and software architecture. The platform is built on Google Cloud and leverages modern cloud-native capabilities while maintaining portability through open standards and reusable engineering patterns.

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 and evolve the enterprise reference architecture and reusable engineering patterns that underpin every AI asset, including common registration schemas, semantic metadata, execution contracts, governance controls, and lifecycle management.
• Design and build the platform’s most technically challenging components, including AI lifecycle registries, AI control-plane services, model abstraction layers, enterprise memory services, retrieval capabilities, and reusable platform APIs.
• Develop the engineering foundations that enable rapid delivery of agentic AI solutions, including reusable scaffolds, frameworks, builder agents, and multi-agent implementation patterns.
• Deliver platform capabilities as intelligent agents where appropriate, enabling the platform to automate its own lifecycle management, planning, governance, and operational workflows.
• Establish engineering standards for software quality, testing, CI/CD, Infrastructure as Code, observability, security-by-default, and AI-assisted software development across the platform team.
• Drive platform portability through open standards, containerization, standardized telemetry, metadata, model serving, and cloud-native engineering practices that avoid unnecessary vendor lock-in.
• Review complex designs and production code, mentor senior engineers, and help establish a culture of technical excellence across the organization.
• Build production-quality software while leveraging AI-assisted engineering throughout the software development lifecycle.

What You’ll Bring Required Skills

• Significant experience designing and building production-scale software platforms, developer platforms, AI platforms, or distributed systems in cloud-native environments.
• Deep expertise in full-stack software engineering, including backend services, APIs, distributed systems, and modern engineering pract…