Partner Solutions Architect, AI Infrastructure and Sovereign Cloud, Google Cloud

As a Partner Solutions Architect, you will serve as a trusted advisor to System Integrator (SI) and Independent Software Vendor (ISV) partners. Drawing on your deep technical expertise in modern public cloud platforms—with a focus on AI infrastructure and Sovereign Cloud—you will enable partners to design and verify solutions that solve complex joint customer technical and business challenges. Leveraging Google Cloud's technical differentiators, you will lead the implementation and integration of partner solutions across Google Cloud's AI infrastructure and Sovereign Cloud product portfolio.

In this role, you will collaborate closely with Partner Development Managers, Product, Engineering, and Go-to-Market (GTM) teams to build and scale relationships across all levels of partner organizations, including C-level executives, Heads of Product and Engineering, and their engineering teams. You will drive internal and external knowledge sharing by creating and curating reusable technical assets such as reference architectures, detailed solution guides, accelerators, blog posts, and demonstrations. Additionally, you will demonstrate thought leadership by representing Google Cloud and our strategic partners at industry events, presenting joint solutions that drive successful product launches and accelerate the adoption of partner solutions built on Google Cloud.

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.

Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

US: $183000 - $265000 (USD) + 20% bonus target + equity + benefits

Learn more about benefits at Google.

Responsibilities

• Work closely with SIs and ISVs to scale platforms focused on Sovereign, AI Infrastructure, and Security capabilities, building joint product capabilities across solution pillars such as AI, Sovereign, Security, DevOps, and Analytics.
• Act as the technical bridge for Google Product and Engineering teams, creating new integrations, resolving blockers, shaping roadmaps, and unblocking partner technical challenges related to Sovereign Cloud deployment and operations.
  
• Lead and participate in deep technical discussions and workshops to identify and qualify business opportunities, comprehending partner technical objectives and capabilities.
  
• Recommend integration strategies, enterprise architectures, platforms, and application infrastructure to build joint Sovereign Cloud and AI solutions, while developing reusable reference architectures, automation scripts, and deployment guides.
  
• Coordinate and influence stakeholders across Product, Engineering, Solutions, Professional Services, and DevRel to align on roadmaps, share partner insights, and resolve technical impediments.

Minimum qualifications:

• Bachelor's degree in Computer Science or equivalent practical experience.
• 7 years of experience in customer or partner-facing technical roles, within a cloud computing environment.
• Experience in data center deploying GPUs, cloud solutions architecture across Kubernetes based applications and Networking.
• Experience building and operationalizing ML models.
  
• Experience with GPU architecture, high performance Data Center Networking, Containerization and Kubernetes.
• Experience collaborating with or within technology ISVs or SaaS companies, leveraging AI/ML technologies in a public cloud environment.
  

Preferred qualifications:

• Expertise with automation tools such as Terraform or Ansible.
• Proficiency in one or more programming languages: SQL, PyTorch, TensorFlow, Pandas, R and Python.
• Understanding AI Models, Large Language Models, AI Agentic workflows, AI specialized infrastructure and AI Accelerators.
• Deep technical knowledge and experience in building AI solutions using Google Cloud products and services (e.g., VertexAI, Gemini Enterprise, ADK).
• Ability to build relationships across technical and business functions within multiple organizations.
• Ability to architect developer training curriculums, lead technical workshops, write developer guides, and build out customer-facing technical labs.