Field Solutions Architect III, Google Cloud, Generative AI

Minimum qualifications:

• Bachelor's degree in Science, Technology, Engineering, Mathematics, or equivalent practical experience.
  
• 8 years of experience in Python or other programming languages in machine learning (e.g., Java, C++, Go).
• Experience in applied AI, with a focus on designing and evaluating systems around foundation 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.

Preferred qualifications:

• Master's degree in Computer Science, Engineering, or a related technical field.
• Experience training and fine tuning models in large scale environments (e.g., image, language, recommendation) with accelerators.
• 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.
• Ability to demonstrate a founder or startup CTO mentality with a bias for action and applying product insights to solve immediate customer issues and unlock long-term value.

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 be focused on frontier AI, including Generative Artificial Intelligence (GenAI), in a technical customer-facing role. You will be responsible to bridge the gap between our AI products and the customer's business problems. Unlike a traditional sales engineer, you will own 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. You will require an understanding of technical skill, problem-solving, and excellent communication with significant customer collaboration and travel often as required. Your primary responsibility will be to construct rapid prototype Generative AI applications tailored to Google Cloud customers, catering to various clientele ranging from early stage startups to prominent, established companies. You will be a hybrid professional, blending the core competencies of an engineer with an aptitude for customer engagement and problem-solving. This will often require you to lead with 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 our product and engineering teams to eliminate obstacles and shape the future trajectory of our offerings. You will be adept at disseminating lessons learned to customers and internal Google teams, translating one-off customer solutions into reusable 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.

Responsibilities

• Be a trusted advisor to customers by understanding their business process and objectives. Design and build end-to-end GenAI-driven solutions spanning AI, Data, and Infrastructure. Work with peers to include the full cloud stack into overall architecture.
• 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, 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.
• 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 closely with product and partner organizations on external enablement. Travel as needed.