Staff AI Solutions Deployment Developer, Applied AI

Cloud Applied AI (AAI) powers business growth with Gemini Enterprise. Our portfolio includes Gemini Enterprise for Customer Experience, along with other vertical and domain packaged solutions. We enable high adoption and speed to value by building solutions that are quickly deployed, delivering new 0-to-1 capabilities with startup agility. Team members operate at the forefront of AI, collaborating directly with model builders with unprecedented speed. As an AI Solutions Deployment Manager, you will be technical solutions consultant across multiple customer deployments, and a high-visibility team multiplier responsible for turning the promise of Agentic AI into production reality for Google’s most strategic global customers. You will sit at the connection of product, development, and the customer, translating complex business friction into elegant, reasoning-based AI solutions. In this role, you will contribute to the end-to-end delivery of agentic solutions, working with a group of deployment specialists to implement best practices for delivering at scale and velocity. You will be a builder, a strategic systems thinker, and a master of navigating complex technical and business challenges. You will be working with a group of deployment specialists, incubating new products and defining the best practices that will enable the entire Google Cloud ecosystem. Individual pay is determined by factors including job-related skills, experience, and relevant education or training. US: $183000 - $265000 (USD) + 20% bonus target + equity + benefits Canada: $204000 - $209000 (CAD) + 20% bonus target + equity + benefits Learn more about benefits at Google . Contribute to technical engagements from 0-to-1, defining the agent’s brain (reasoning paths), hands (API tools), and guardrails to ensure resilient, autonomous infrastructure. Mitigate high-stakes customer escalations, providing direct debugging and architectural guidance to customer developers and C-suite teams. Deploy pre-general availability features in real-world environments to stress-test capabilities, synthesizing field intelligence to directly influence the Google Cloud Product and Developer roadmaps. Advocate for foundational architectural frameworks such as Agent-MVC and pioneer advanced techniques like cyclic reasoning loops to accelerate time-to-value. Bridge the gap between non-deterministic LLM outputs and deterministic enterprise systems (e.g., SAP, Salesforce) to ensure safe, reliable agentic operations within guardrails. Minimum qualifications: Bachelor's degree in Science, Technology, Engineering, Mathematics, or equivalent practical experience. 8 years of experience in customer-facing technical roles (e.g., Solutions Architect, Forward Deployed Specialist, Principal Consultant) with a focus on enterprise systems. 3 years of experience writing code in one or more programming languages (e.g., Python, Java). Experience with Large Language Models (LLMs), prompt development, and conversational AI frameworks. Preferred qualifications: Master's degree in Engineering, Computer Science, or related technical fields. Experience building and scaling complex production systems and working with multi-stakeholder technical projects, and with Google Cloud Platform (GCP) environment setup and cloud architecture. Experience with telecom customer deployments, and other similar industries. Experience in technical consulting, navigating the governance, security, and procurement complexities. Ability to bridge the gap between LLM capabilities and deterministic enterprise systems (e.g., Salesforce, SAP). Ability to lead cross-functional teams and influence executive stakeholders in large, cross-functional organizations. Bachelor's degree in Science, Technology, Engineering, Mathematics, or equivalent practical experience. 8 years of experience in customer-facing technical roles (e.g., Solutions Architect, Forward Deployed Specialist, Principal Consultant) with a focus on enterprise systems. 3 years of experience writing code in one or more programming languages (e.g., Python, Java). Experience with Large Language Models (LLMs), prompt development, and conversational AI frameworks.