Field Solutions Architect, Applied Artificial Intelligence, Google Cloud

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The Google Cloud Consulting Professional Services team guides customers through the moments that matter most in their cloud journey to help businesses thrive. We help customers transform and evolve their business through the use of Google’s global network, web-scale data centers, and software infrastructure. As part of an innovative team in this rapidly growing business, you will help shape the future of businesses of all sizes and use technology to connect with customers, employees, and partners.

As a Field Solutions Architect (FSA) in Applied AI, you are the "Agent Engineer" and the primary delivery arm 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. You role is a high-travel and impactful 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 require an in-depth understanding of software engineering, MLOps, and cloud infrastructure.

Google Cloud accelerates every organization’s ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google’s cutting-edge technology, and tools that help developers build more sustainably. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems.

The US base salary range for this full-time position is $97,500-$140,000 + 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 complex Conversational AI and Customer Experience (CX) applications, transitioning from rapid prototypes to production-grade agentic workflows 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 complex 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.

Minimum qualifications:

Bachelor’s degree in Computer Science or equivalent practical experience in Software Engineering, SRE, or DevOps.
Experience in Python, and architecting scalable AI systems on cloud platforms.
Experience deploying conversational agents using code-based frameworks.
Experience deploying resources via Terraform or similar tools, and automating the setup of agents, functions, and networking.
Experience building full-stack applications that interact with enterprise IT infrastructures.

Preferred qualifications:

Master’s degree in AI, Computer Science, or a related technical field.
Experience troubleshooting live, high-traffic systems during critical windows, and in developing and driving customer projects forward in a timely manner.
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.
Experience implementing multi-agent systems using frameworks like ReAct, self-reflection, and hierarchical delegation.
Ability to build in real-time with customers utilizing modern generative AI tools, and travel up to 50% of the time.