About the job

The Google Cloud Platform team helps customers transform and build what's next for their business — all with technology built in the cloud. Our products are developed for security, reliability and scalability, running the full stack from infrastructure to applications to devices and hardware. Our teams are dedicated to helping our customers — developers, small and large businesses, educational institutions and government agencies — see the benefits of our technology come to life. As part of an entrepreneurial team in this rapidly growing business, you will play a key role in understanding the needs of our customers and help shape the future of businesses of all sizes use technology to connect with customers, employees and partners.

The Google Cloud Platform team helps customers transform and build what's next for their business — all with technology built in the cloud. As a Principal Partner AI Architect, you will be the technical "tip of the spear" for Google’s AI partnership strategy. You will act as an entrepreneurial architect, responsible for making Gemini Enterprise a reality within our partners' unique environments. You won't just provide support; you will co-build and solve the "impossible" technical hurdles that prevent partners from reaching production at scale. Your work will directly impact how the world's largest organizations adopt Google’s AI ecosystem, accelerating the transition from pilot projects to enterprise-wide production value.

Google Cloud accelerates every organization’s ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google’s cutting-edge technology, and tools that help developers build more sustainably. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems.

The US base salary range for this full-time position is $234,000-$325,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.

Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google.

Responsibilities

• Lead deep technical co-innovation with Google Cloud’s most strategic partners to architect and deploy generative AI solutions (e.g., Gemini Enterprise) in high-scale environments.

• Identify and solve architectural bottlenecks that hinder partner deployment velocity, ranging from model steering and API performance to hybrid-cloud integration constraints.

• Productize bespoke integration work into versioned, validated deployment baselines that can be scaled across the global partner ecosystem.

• Act as an entrepreneurial technical lead, guiding partner CTOs and Principal Engineers on the "art of the possible" and long-term AI strategy.

• Collaborate with internal product and engineering teams to feed partner requirements back into the development cycle, ensuring Google’s AI tools are enterprise-ready.

Minimum qualifications

• Bachelor’s degree in Computer Science, STEM, or equivalent practical experience

• 10 years of experience in cloud computing architecture, software engineering, or technical consulting

• Experience in a partner-facing, customer-facing, or product management role bridging technical execution with business strategy

• Experience in either system design or reading code (e.g., Java, C++, Python)

Preferred qualifications

• 5 years of experience architecting or leading the deployment of Enterprise AI/ML solutions, with deep expertise in Multi-Agent Systems (MAS), reasoning engines, and advanced model steering

• Experience resolving complex integration hurdles in Identity Orchestration (OAuth 2.0, OIDC, SAML) and cross-tenant security hardening (VPC-SC, IAM) within multi-tenant SaaS environments

• Experience with production-grade RAG, Vector Databases (ScaNN/HNSW), and high-scale data interoperability (Big Query Zero-Copy, AlloyDB AI)

• Experience defining user journeys, conducting "gap analysis" on technical platforms, and translating partner friction into prioritized PRDs to influence internal engineering roadmaps