About the job

As a Field Solutions Architect (FSA), you will play a pivotal role in the Customer Engineering team working with a strategic customer. You will be focused on frontier AI Infrastructure, Frameworks, Generative AI (GenAI), and Distributed Systems/Applications, in a highly-technical customer-facing role. The FSA’s primary responsibility is to bridge the gap between the cutting-edge AI products and the customer's real-world business problems. Unlike a traditional sales engineer, the FSA owns the end-to-end technical delivery, from understanding a client's needs to architecting, building, and deploying solutions directly for or on the customer's infrastructure. This role requires a blend of deep technical skill, strategic problem-solving, and strong communication with significant customer collaboration and travel often required.

Your primary responsibility will be to construct rapid prototype solutions tailored to strategic Google Cloud customers, catering to various clientele ranging from early stage ideation, proof-of-concept, all the way to driving efficiencies and operationalizing global scale systems.

You will be a hybrid professional, blending the core competencies of an engineer with an aptitude for customer engagement and strategic problem-solving. This will often require you to lead with deep, bespoke implementation as the primary value proposition, ensuring that our core technology delivers demonstrable value in the customer's unique operational context.

You will have close collaboration with the Product and Engineering teams to eliminate obstacles and shape the future trajectory of the offerings. You will be adept at disseminating lessons learned to customers and internal Google teams, 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 $183,000-$265,000 + 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 process and objectives. Design and build end-to-end solutions spanning AI, Data, and Infrastructure. Work with peers to include the full cloud stack into overall architecture.

• Rapidly build production-grade prototypes that deliver measurable outcomes (e.g., write custom code, integrate disparate data sources, design data ontologies, deploy solutions on customer infrastructure).

• Represent the customer, gather 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.

• Work cross-functionally to influence Google Cloud strategy and product direction at the intersection of infrastructure and AI/ML by advocating for customer requirements.

• Coordinate regional field enablement with leadership and work with product and partner organizations on external enablement. Travel as needed.

Minimum qualifications

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

• 8 years of experience in Python, Golang, or other programming language(s) used in Machine Learning (ML).

• Experience in Solution Architecture, Software Engineering or Systems Engineering.

• Experience architecting, deploying, and managing solutions on a cloud platform.

• Experience with AI Infrastructure (e.g. TPUs/GPUs)

Preferred qualifications

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

• Experience with distributed training and optimizing performance versus costs.

• Experience in systems design with the ability to architect and explain data pipelines, ML pipelines, and ML training and serving approaches.

• Experience training and fine tuning models in large-scale environments (e.g., image, language, recommendation) with accelerators.

• Ability to have a founder or startup CTO mentality with a bias for action and applying strategic product insights to solve immediate customer challenges and unlock long-term value.