Google Cloud is seeking an innovative Generative AI Forward Deployed Engineer. This role involves deep collaboration with customers to build and deploy cutting-edge AI solutions directly within their environments. You will act as an "innovator-builder," focusing on practical implementation and problem-solving to bring AI to enterprise maturity. This is an opportunity to shape the future of AI adoption by bridging advanced Google AI products with real-world business needs. Join a dynamic team driving the AI revolution for businesses globally, leveraging Google's leading AI portfolio and collaborative culture.

As part of the Go-To-Market team, you will contribute to Google Cloud's mission by enabling customer success and defining the next era of cloud innovation. You'll utilize Google's robust AI capabilities, including Gemini models and the Vertex AI platform, to address complex business challenges and foster widespread AI adoption.

Develop complex AI applications, transforming rapid prototypes into production-ready agentic workflows that deliver measurable ROI. Architect and code the essential integrations between Google's AI offerings and customer infrastructure, including APIs, data silos, and security protocols.

Construct high-performance evaluation and observability frameworks to ensure agentic systems meet stringent requirements for accuracy, safety, and latency. Identify recurring patterns and challenges in AI deployment, translating them into reusable components or feature requests for engineering teams.

Collaborate closely with customer engineering teams to implement Google-grade development best practices, driving successful project outcomes and user adoption.

A Bachelor's degree in Engineering, Computer Science, or a related field, or equivalent practical experience, is required. You should possess 8 years of experience in cloud computing or technical customer-facing roles.

Demonstrated experience in building data pipelines for structured and unstructured data, utilizing vector databases and RAG architectures for enterprise AI. Proven ability to take production-grade AI solutions from concept to launch, including architecting AI systems on cloud platforms like GCP. Experience leading technical discovery sessions is also essential.

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