About the job

As a Field Solutions Architect (FSA), you will play a pivotal role in the Google Cloud AI Go-To-Market organization. You will focus on frontier AI, including Generative AI (GenAI), in a highly technical customer-facing role. The FSA’s primary responsibility is to bridge the gap between our AI products and customers' real-world business problems. Unlike a traditional sales engineer, the FSA owns end-to-end technical delivery from understanding client needs to architecting, building, and deploying solutions directly on customer infrastructure.

This role requires a blend of technical expertise, problem-solving, and communication, with significant customer collaboration and travel. You will construct rapid-prototype Generative AI applications tailored to Google Cloud customers, ranging from early-stage startups to prominent, established companies. As a hybrid professional, you will blend engineering core competencies with an aptitude for customer engagement and problem-solving. This often involves leading with bespoke implementation as the primary value proposition, ensuring our core technology delivers demonstrable value within the customer's unique operational context. Finally, you will collaborate closely with product and engineering teams to eliminate obstacles and shape the future trajectory of our offerings, translating one-off customer solutions into reusable, scalable assets.

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 $123,000-$176,000 + 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

• Be a trusted advisor to customers by understanding their business processes and objectives. Design and build GenAI-driven solutions spanning AI, Data, and Infrastructure. Work with peers to include the full cloud stack into overall architecture.

• Build production-grade prototypes rapidly to deliver measurable outcomes, including writing custom code, integrating disparate data sources, designing data ontologies, and deploying solutions on customer infrastructure.

• Represent the customer, gathering real-time feedback and insights. Formalize and abstract field-tested solutions into reusable modules or new product features to drive product innovation. Establish technical and business cases to support recommendations.

Minimum qualifications

• Bachelor's degree in Science, Technology, Engineering, Mathematics, or equivalent practical experience.

• 3 years of experience in Python and relevant machine learning packages (e.g., Keras, Pytorch, HF Transformers).

• Experience in applied AI, with a focus on building systems around pretrained models (e.g., prompt engineering, fine-tuning, RAG, orchestrating model interactions with external tools to deliver solutions).

• Experience architecting, deploying, or managing solutions on a Cloud Platform (e.g., Google Cloud Platform).

Preferred qualifications

• Master's degree in Computer Science, Engineering, or a related technical field.

• Experience working with customers in a technical capacity.

• Experience in systems design with the ability to architect data and ML pipelines, including advanced agentic patterns such as "Plan-and-Execute," multi-agent collaboration, and self-reflection loops.

• Experience in software engineering management or project/program management.

• Experience training and fine-tuning models in large-scale environments using accelerators, with a proven track record of deploying production-grade AI agents that utilize tool-use (function calling) and state management.

• Ability to maintain a startup mentality and a bias for action focused on customer solutions.