As a Gemini Enterprise Customer Experience (GECX) Forward Deployed Engineer (FDE) in Applied AI, you are the agent engineer and the primary driver for our customers most critical AI initiatives. You will take initial conversational prototypes and transform them into production-ready solutions, owning the end-to-end engineering life-cycle, including the transition from art of the possible to real-world business value and scalable, secure AI systems. You will have an understanding of software engineering, machine learning operations, and cloud infrastructure. This is a high-travel, high-impact role focused on leading technical delivery for conversational AI pilots and establishing the first Customer User Journeys (CUJs) for our largest customers at their sites.It's an exciting time to join Google Cloud’s Go-To-Market team, leading the AI revolution for businesses worldwide. You’ll excel by leveraging Google's brand credibility—a legacy built on inventing foundational technologies and proven at scale. We’ll provide you with the world's most advanced AI portfolio, including frontier Gemini models, and the complete Vertex AI platform, helping you to solve business problems. We’re a collaborative culture providing direct access to DeepMind's engineering and research minds, empowering you to solve customer challenges. Join us to be the catalyst for our mission, drive customer success, and define the new cloud era—the market is yours.
- Serve as the lead developer for conversational Artificial Intelligence (AI) and Customer Experience (CX) applications, transitioning from rapid prototypes to production-grade agentic workflows.
- Architect and code conversational flows that are not just functional, but optimized for the connective tissue between Google’s conversational AI products and customers live infrastructure, including APIs, legacy data silos, and security perimeters.
- Build high-performance Evaluation (Eval) pipelines and observability frameworks to optimize agentic workloads, focusing on reasoning loops, tool selection, and reducing latency while maintaining production-grade security and networking.
- Identify repeatable field patterns and technical friction points in Google’s Applied AI (AAI) stack, converting them into reusable modules or product feature requests for Engineering teams.
- Co-build with Customer Engineering teams to instill Google-grade development best practices, ensuring long-term project success and high end-user adoption.
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