Customer Engineer, Platform, SLED, Higher Education, Public Sector

Job description

Collaborate with the Sales team to uncover and qualify Google Cloud and AI-first opportunities, proactively addressing technical objections around adoption, and developing pathways to clear technical blockers. Lead the technical relationship with our premier Higher Education customers in California, leading immersive product briefings on Google Cloud and Google's GenAI portfolio, driving innovative, agentic proof-of-concept (PoC) work, and orchestrating specialized technical resources across Google Public Sector. Demonstrate and rapidly prototype intelligent integrations, bringing autonomous agents and AI-powered workflows to life directly within complex customer and partner environments. Design and recommend AI-optimized enterprise architectures and integration strategies, laying the secure platform and data infrastructure foundations required to successfully deploy comprehensive, forward-looking solutions using Google Cloud best practices. Minimum Qualifications: Bachelor's degree or equivalent practical experience. 10 years of experience with cloud-native architecture in a customer-facing or support role. Experience engaging with and presenting to technical stakeholders and executive leaders. Experience with cloud infrastructure engineering, on-premise environments, virtualization, or containerization platforms. Ability to travel up to 30% of the time to university campuses, industry conferences, and related events as required to advocate Google Public Sector's goals and engage directly with educational leaders. Preferred Qualifications: Experience partnering with Higher Education customers, specifically R1 and R2 institutions, to drive digital transformation and AI-readiness. Experience architecting innovative solutions and building compelling demonstrations using generative AI technologies, specifically Gemini and autonomous agentic workflows. Expertise migrating and modernizing legacy applications to scalable cloud platforms, laying the essential data groundwork for advanced AI/ML workloads. Understanding of enterprise security and complex networking principles as needed to secure AI pipelines, LLM deployments, and distributed architectures. Proven agility to rapidly learn and adapt to the changing evolution of emerging AI methodologies and new Google Cloud solutions.