Field Solutions Architect II, Google Cloud, Generative AI

As a Field Solutions Architect, you will play a pivotal role in the Google Cloud AI Go-To-Market organization. You will be focused on frontier AI, including Generative AI (GenAI), in a highly-technical customer-facing role. You will bridge the gap between our cutting-edge AI products and the customer's real-world business problems. 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 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 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, 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 $147,000-$216,000 + 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 by understanding customer business processes and objectives. Design and build end-to-end Generative AI solutions and multi-agent orchestrations, across AI, Data, and Infrastructure. Work with peers to integrate the full cloud stack.
• Build production-grade prototypes that deliver measurable outcomes. This includes writing custom code for autonomous agentic workflows, integrating data sources, designing data ontologies, and deploying solutions directly on customer infrastructure.
• Represent the customer, gathering real-time feedback and insights. Formalize field-tested solutions into reusable modules or core features to drive innovation. Establish technical and business cases to support your recommendations. 
• Influence Google Cloud strategy and product direction by advocating for enterprise customer requirements, specifically around agentic reliability and reasoning accuracy.
• Coordinate regional field enablement with leadership and work closely 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.
• 6 years of experience in Python and relevant machine learning packages (e.g. Keras, Pytorch, HF Transformers).
• Experience in applied AI, with a focus on designing and evaluating systems around foundation models. This includes prompt engineering, fine-tuning, retrieval-augmented generation (RAG), and orchestrating model interactions with external tools to deliver complete solutions.
• Experience designing and implementing autonomous agents and multi-agent systems to automate multi-step business workflows.
• 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 with accelerators, with a track record of deploying production-grade AI agents that manage tool-use (function calling) and complex state management.
• 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.