Job Description

Minimum qualifications:

• Bachelor’s degree in Engineering, Computer Science, a related field, or equivalent practical experience.
• 3 years of experience building and shipping production-grade solutions to external or internal customers using Python, Typescript or comparable languages
• Experience building pipelines for structured, unstructured data, incorporating vector databases and Retrieval-Augmented Generation (RAG) like architectures to power enterprise-grade AI solutions.
• Experience leading technical discovery sessions with business stakeholders and engineering teams to define technology and hardware infrastructure requirements.
• Experience in architecting technology solutions that ensure data sovereignty, GDPR compliance, and secure model governance.

Preferred qualifications:

• Master’s degree or PhD in Computer Science, or a related technical field.
• Experience architecting integrated systems, navigating real-time inference constraints, and implementing model quantization for resource-constrained environments.
• Experience in optimizing state management and granular tracing, or to maximize throughput and minimize compute wastage with content generation at scale by leveraging ones knowledge of model serving metrics
• Experience in architecting and scaling production-grade ML systems in complex enterprise environments, workflow pipelines to implement CI/CD/CT automation and experimentation.
• Experience with GenMedia models and fine-tuning capability to ensure hyper-realistic, brand-consistent content across image, video and audio.

About the job

The Google Cloud Platform team helps customers transform and build what's next for their business — all with technology built in the cloud. Our products are developed for security, reliability and scalability, running the full stack from infrastructure to applications to devices and hardware. Our teams are dedicated to helping our customers — developers, small and large businesses, educational institutions and government agencies — see the benefits of our technology come to life. As part of an entrepreneurial team in this rapidly growing business, you will play a key role in understanding the needs of our customers and help shape the future of businesses of all sizes use technology to connect with customers, employees and partners.

As a Generative AI Forward Deployed Architect at Google Cloud, you will be an embedded builder who bridges the gap between frontier AI solutions. Unlike traditional advisory roles, you will function as an innovator-builder moving beyond high-level architecture to develop and provide reference agentic solutions for the customer. You will handle blockers including solving the integration complexities, data readiness issues, and state-management issues that prevent Google’s latest and most advanced technology from reaching enterprise-grade maturity. By embedding with strategic accounts, you will serve a dual purpose offering reference solutions to enable customers to deploy Google’s latest and most advanced technologies and acting as a critical feedback loop, transforming real-world field insights into Google Cloud’s future product roadmap.

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.

Responsibilities

• Serve as a developer of complex reference solutions to enable customers to deploy Google’s latest and most advanced technologies.
• Architect and develop reference prototypes being the connective tissue between Google’s advanced cloud solutions and customer's live infrastructure, including APIs, legacy data silos, and security perimeters as part of an expert team.
• Build high-performance evaluation pipelines and observability frameworks to ensure agentic systems meet requirements for accuracy, safety and latency.
• Identify repeatable field patterns and friction points within existing Google solutions, converting them into reusable modules or formal product feature requests for the Engineering teams.
• Collaborate with Solution Architect teams to instill Google-grade development best practices, ensuring long-term project success and high end-user adoption.