As a Forward Deployed Engineer (FDE) in Applied AI, you will be 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 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. You will have 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.
The US base salary range for this full-time position is $147,000-$211,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.
Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google.
Responsibilities
- Serve as the lead developer for conversational AI and customer experience 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.
- 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 project success and end-user adoption.
Qualifications
Minimum qualifications:
- Bachelor’s degree in Engineering, Computer Science, a related field, or equivalent practical experience.
- 2 years of experience with software development using Python or similar coding languages.
- 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.
- Experience architecting AI systems on cloud platforms (e.g., Google Cloud Platform (GCP).
Preferred qualifications:
- 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 connecting agents to enterprise knowledge bases and optimizing retrieval-augmented generation (RAG) chunking to prevent hallucinations.
- Experience debugging agent logic and optimizing tool selection, including tracing conversation identifications (IDs) across microservices to identify and resolve failures in real-time.
- Track record of troubleshooting live, high-traffic systems during critical windows.
- Ability to travel up to 50% of the time.