Join Google Cloud as a Generative AI Forward Deployed Engineer, an embedded builder revolutionizing AI implementation for customers. This role transcends traditional advisory positions, focusing on hands-on development to transform frontier AI into production-ready solutions.

You will act as an "innovator-builder," moving beyond high-level strategies to actively code, debug, and deploy bespoke agentic solutions directly within client environments. This is an opportunity for high-agency engineers with an entrepreneurial spirit to tackle complex integration, data readiness, and state-management challenges.

By embedding with strategic accounts, you'll provide "white glove" deployment of advanced AI systems. Simultaneously, you'll serve as a crucial feedback channel, translating real-world insights into Google Cloud's future product development. This is a pivotal moment to lead the AI revolution, leveraging Google's trusted brand and cutting-edge AI portfolio, including Gemini models and the Vertex AI platform, to solve critical business problems.

Develop complex AI applications, evolving rapid prototypes into production-grade agentic workflows that deliver significant return on investment.

Engineer the essential connections between Google's AI offerings and customer infrastructure, integrating APIs, legacy data systems, and security protocols.

Construct robust evaluation pipelines and observability frameworks to guarantee AI agent systems meet stringent accuracy, safety, and latency standards.

Identify recurring patterns and challenges within Google's AI stack in the field, transforming them into reusable components or formal feature requests for engineering teams.

Collaborate closely with customer engineering teams to implement Google-standard development best practices, fostering project longevity and user adoption.

A Bachelor's degree in Engineering, Computer Science, or a related field, or equivalent practical experience is required.

We seek candidates with 8 years of experience in cloud computing or a technical customer-facing role.

Demonstrated experience in building data pipelines for both structured and unstructured data, utilizing vector databases and RAG architectures for enterprise AI solutions.

Proven ability to take production-grade AI-driven solutions from concept through launch, and architect AI systems on cloud platforms like Google Cloud Platform (GCP).

Experience leading technical discovery sessions is essential.

Preferred qualifications include a Master's or PhD in AI, Computer Science, or a related technical field, along with experience in multi-agent systems using frameworks such as LangGraph or CrewAI, and knowledge of "LLM-native" metrics and optimization techniques.