Forward Deployed Engineer, Applied AI, Google Cloud

As a Forward Deployed Engineer (FDE) in Applied AI, you are the “Agent Engineer” and the primary driver for customers’ most critical AI initiatives. You 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. In this role, you will focus on leading technical delivery for Conversational AI pilots and establishing the first Customer User Journeys (CUJs) for customers at their sites. Your role requires a deep 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 leverage 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.

Responsibilities

• Serve as the lead developer for complex Conversational AI and Customer Experience (CX) applications, transitioning from rapid prototypes to production-grade agentic workflows (e.g., multi-agent systems, Model Context Protocol (MCP) servers) that drive measurable Return on Investment (ROI).
• 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.
• Collaborate with customer engineering teams to instill Google-grade development best practices, ensuring long-term project success and high end-user adoption.

Google is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. See also Google’s EEO Policy and EEO is the Law. If you have a disability or special need that requires accommodation, please let us know by completing our Accommodations for Applicants form.

Do you have experience in Windows?, Do you have a Master’s degree?, * Bachelor’s degree in Engineering, Computer Science, a related field, or equivalent practical experience.

• 8 years of experience with software development using Python or similar coding languages.
• Experience architecting AI systems on cloud platforms (e.g., Google Cloud Platform (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., * Master’s 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 troubleshoot live, high-traffic systems during critical windows.
• Ability to travel up to 50% of the time.