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Forward Deployed Engineer III, Google Cloud, Applied AI

Google
15 hours ago
Full-time
On-site
San Francisco, GA
$174,000 - $253,000 USD yearly

JobsCloseBy Editorial Insights

Google Cloud Applied AI seeks a Forward Deployed Engineer III to lead end-to-end delivery of production-grade conversational AI for enterprise customers. You’ll scale early prototypes into live, secure solutions, owning the lifecycle from design to deployment, with travel up to 50%. Expect GCP-based architecture, Terraform automation, and full-stack integrations with enterprise IT, APIs, and data silos. Requirements include a bachelor’s in engineering or CS and 5 years Python or equivalent; cloud AI experience; and collaboration with customer teams. Preferred: advanced degrees and hands-on work with multi-agent systems, RAG, and real-time debugging. To apply, emphasize impact, production readiness, customer outcomes, travel willingness, and cross-team collaboration.


Minimum qualifications:

  • Bachelor’s degree in Engineering, Computer Science, a related field, or equivalent practical experience.
  • 5 years of experience with software development using Python or similar coding languages.
  • Experience architecting AI systems on cloud platforms (e.g., GCP).
  • Experience deploying resources via Terraform or similar tools to automate the setup of agents, functions, or networking.
  • Experience building full-stack applications that interact with enterprise IT infrastructures and developing external customer projects.

Preferred qualifications:

  • Master’s degree or PhD in AI, Computer Science, or a related technical field.
  • Experience implementing multi-agent systems using frameworks like ReAct and self-reflection.
  • Experience debugging Agent logic and optimizing tool selection, including tracing conversation IDs across microservices to identify and resolve failures in real-time.
  • Experience connecting agents to enterprise knowledge bases and optimizing RAG chunking to prevent hallucinations.
  • Ability to travel up to 50% of the time.
  • Track record of troubleshooting live, high-traffic systems during critical windows.

About the job

As a Forward Deployed Engineer (FDE) in Applied AI, you are the "Agent Engineer" and the primary driver for our customers' most critical AI initiatives. You take initial conversational prototypes and transform them into production-ready solutions, owning the end-to-end engineering lifecycle, including the transition from "Art of the Possible" to real-world business value and scalable, secure AI systems. 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. This role requires an understanding of software engineering, machine learning operations, and cloud infrastructure.

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.

Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

US: $174000 - $253000 (USD) + 15% bonus target + equity + benefits

Learn more about benefits at Google.

Responsibilities

  • 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 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.