O

OpenAI

Builds AI models and the ChatGPT assistant

Partner Applied AI Engineer - Google Cloud

Implementation & Technical Account ManagementPosted on Nov 11
Full-timeSan Francisco, CA, USA$197k - $278k /yrHybrid

About the Job

Requirements

  • Have substantial technical consulting or equivalent experience, including relationships with senior technical and business leaders in complex organizations.
  • Have hands-on experience with Model Garden and the Vertex AI SDK for Python / Agent Platform SDK, including programmatic model discovery, deployment configuration, endpoint deployment, and inference testing.
  • Understand how the Vertex AI SDK / Agent Platform SDK differs from the Google Gen AI SDK and OpenAI API/SDK integration paths.
  • Be able to build production applications using Python or another relevant language, APIs, containers, and cloud services.
  • Have practical experience with large language model evaluation, retrieval, tool use, and secure enterprise integration.

Responsibilities

  • 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.
  • 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.
  • 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.
  • 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.
  • 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, preview limitations, and unresolved gaps, and follow decisions back to the field.

About the Company

OpenAI

Builds AI models and the ChatGPT assistant

OpenAI researches and builds artificial intelligence models and turns them into products for people, developers, and businesses, with a stated mission of making sure artificial general intelligence benefits all of humanity. Its launch of ChatGPT in 2022 brought generative AI into everyday use. People use ChatGPT to ask questions, write, code, and analyze files, developers build OpenAI's models into their own apps through its API, and its Codex agent works through coding tasks on its own. OpenAI earns money from ChatGPT subscriptions, business plans, and API usage. Sam Altman, Elon Musk, and others co-founded it in 2015; it is based in San Francisco and overseen by a nonprofit, the OpenAI Foundation.

Company size
1,001-5,000
Company stage
Late Stage VC
Total funding
$207.5B
Headquarters
San Francisco, CA
Founded
2015

Recently Posted Jobs

Explore more opportunities at OpenAI

See more jobs