Partner Applied AI Engineer, Google Cloud

Details

The Applied AI Engineering team helps developers and enterprises deploy generative AI safely and effectively. This role serves as a technical counterpart to Google Cloud Customer Engineers, AI specialists, and partner technical teams, helping customers move from experimentation to production-scale AI systems.

The position combines hands-on engineering, solution architecture, technical consulting, and ecosystem leadership. You will shape joint technical strategy, lead complex enterprise engagements, build reusable systems and reference implementations, and enable Google Cloud and partner teams to deliver subsequent deployments independently.

Responsibilities

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 opportunities where OpenAI can improve customer outcomes in Google Cloud environments.
• Define engagement models and prioritization frameworks focused on high-impact opportunities and reusable solutions.
• Help teams distinguish Model Garden access, Google Cloud Marketplace purchasing, and direct OpenAI offerings.

Complex Deal Leadership and Execution

• Lead technical strategy for enterprise engagements from use-case discovery and architecture design through prototyping, evaluation, and production deployment.
• Resolve integration and operational blockers with OpenAI and Google Cloud counterparts.
• Validate model and feature availability, regional requirements, access controls, data handling, quotas, and support boundaries.
• Prioritize opportunities and allocate technical resources based on customer impact and ecosystem scalability.

Solution Architecture and Hands-On Building

• Design and build OpenAI-powered applications integrating with Google Cloud services such as Cloud Run, Google Kubernetes Engine, Cloud Storage, and BigQuery.
• Develop reference implementations for supported Model Garden access to OpenAI through the Responses API, as well as direct OpenAI integration patterns where appropriate.
• Use the Vertex AI SDK for Python / Agent Platform SDK to discover deployable Model Garden models, inspect deployment configurations, deploy supported open models to endpoints, and test inference.
• Validate projects, regions, machine and accelerator choices, credentials, permissions, and deployment configurations.
• Maintain supported SDK dependencies and adapt examples as Google updates its SDKs.
• Use the Google Gen AI SDK for Google generative AI capabilities migrated from legacy Vertex AI modules, and the documented OpenAI API or SDK for OpenAI integration paths.
• Build retrieval-augmented generation and tool-using workflows that connect approved enterprise data and services with clear authorization boundaries.
• Establish evaluation, observability, CI/CD, and operational practices addressing output quality, safety, reliability, latency, and cost.
• Incorporate identity, networking, secrets management, and data governance explicitly into architectures.

Ecosystem Enablement and Scale

• Create workshops, demos, playbooks, and reference architectures for Google Cloud teams and systems integrators.
• Teach partner engineers how to evaluate Model Garden models and select deployment approaches.
• Pair with field teams on early deployments and transfer delivery ownership as patterns mature.
• Scale solutions through global and regional systems integrators and independent software vendors while maintaining reusable assets.

Cross-Functional Leadership and Feedback

• Work with Alliances, Product, Engineering, Go To Market, and Enablement teams 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, unresolved gaps, and field decisions.

Requirements

• Substantial technical consulting or equivalent experience, including relationships with senior technical and business leaders in complex organizations.
• 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.
• Understanding of the differences between the Vertex AI SDK, Google Gen AI SDK, and OpenAI API/SDK integration paths.
• Ability to build production applications using Python or another relevant programming language, APIs, containers, and cloud services.
• Practical experience with LLM evaluation, retrieval, tool use, and secure enterprise integration.
• Ability to balance hands-on delivery with strategic influence and build trust with engineers, architects, and executives.
• Preference for repeatable solutions, ownership of ambiguous problems, and measuring success through customer outcomes and partner self-sufficiency.

Benefits

• Base salary of $197,000–$278,000 per year.
• Equity, performance-related bonuses for eligible employees, and comprehensive benefits.
• Medical, dental, and vision insurance, with employer contributions to Health Savings Accounts.
• Flexible spending accounts, commuter benefits, and a 401(k) retirement plan with employer match.
• Paid parental, medical, caregiver, and sick or safe leave.
• Paid time off, company holidays, and office closures.
• Mental health and wellness support, life and disability coverage, and a learning and development stipend.
• Daily office meals and eligible meal delivery credits.
• Relocation support for eligible employees.