Partner Applied AI Engineer, Google Cloud

About the team The Applied AI Engineering team helps developers and enterprises deploy generative AI safely and effectively. We serve as trusted technical advisors, helping customers and partners move from early experimentation to production-scale AI systems. As a Partner Applied AI Engineer focused on Google Cloud, you will help customers build with OpenAI in the cloud environment they already use. You will work with Google Cloud and its partner ecosystem to turn early implementations into repeatable approaches that field teams can deliver independently. About the role We are looking for an experienced technical leader to serve as a primary technical counterpart to Google Cloud Customer Engineers, AI specialists, and partner technical teams. You will shape joint technical strategy, define engagement models, and build systems that scale beyond individual deals. Working across pre- and post-sales, you will guide enterprise customers from discovery through production while enabling Google Cloud and its partners to lead subsequent deployments. This role combines hands-on engineering, technical judgment, and ecosystem leadership. You will bring deep familiarity with Gemini Enterprise Agent Platform (formerly Vertex AI), particularly Model Garden and its Python SDK tooling, and help customers choose supported OpenAI integration paths based on workload requirements, model capabilities, security controls, and operational needs. In this role, you will: Strategic Google Cloud engagement and influence Build trusted relationships with Google Cloud field leadership, Customer Engineers, AI specialists, and partner teams across regions. Influence joint account strategy and identify new opportunities where OpenAI can improve customer outcomes within Google Cloud environments. Define engagement models and prioritization frameworks that concentrate technical effort on high-impact opportunities and reusable solutions. Help teams distinguish model access through Model Garden, purchasing through Google Cloud Marketplace, and direct OpenAI offerings. Complex deal leadership and execution Lead technical strategy for complex enterprise engagements, from use-case discovery and architecture design to prototyping, evaluation, and production deployment. Resolve integration and operational blockers with OpenAI and Google Cloud counterparts, giving account teams clear decisions and next steps. Validate model and feature availability, regional requirements, access controls, data handling, quotas, and support boundaries for each workload. Prioritize opportunities and allocate technical resources according to customer impact and the potential to scale through the ecosystem. Solution architecture and hands-on building Design and build OpenAI-powered applications that integrate with Google Cloud services, including Cloud Run or Google Kubernetes Engine, Cloud Storage, and BigQuery where appropriate. Develop reference implementations for supported Model Garden access to OpenAI through the Responses API, alongside direct OpenAI integration patterns where those better meet customer requirements. Use the Vertex AI SDK for Python / Agent Platform SDK to discover deployable Model Garden models, inspect deployment configurations, and deploy supported open models to endpoints. Validate project, region, machine and accelerator choices, credentials, permissions, and test inference; distinguish this workflow from managed model API access. Maintain supported SDK dependencies and adapt examples as Google updates its SDKs. Use Google Gen AI SDK for Google generative-AI capabilities migrated out of legacy Vertex AI modules, and the documented OpenAI API or SDK for OpenAI integration paths. Build retrieval-augmented generation and tool-using workflows, connecting approved enterprise data and services with clear authorization boundaries. Establish evaluation, observability, CI/CD, and operational practices that address output quality, safety, reliability, latency, and cost. Make identity, networking, secrets management, and data governance explicit in architectures rather than assuming controls transfer between services or providers. Ecosystem enablement and scale Create workshops, demos, playbooks, and reference architectures that Google Cloud teams and systems integrators can use independently. Teach partner engineers how to evaluate model choices in Model Garden and select deployment approaches based on customer needs. Pair with field teams on early deployments, then transfer delivery ownership as patterns mature. Scale through global and regional systems integrators and independent software vendors, maintaining reusable assets as integrations evolve. Cross-functional leadership and feedback Work with Alliances, Product, Engineering, GTM, and Enablement to align technical priorities and execution. Turn recurring customer and partner issues into evidence-backed product feedback and specific requests. Maintain clear guidance on validated capabilities, prev