Senior AI Solution Builder, Cloud Learning Services
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Minimum qualifications:
- Bachelor's degree or equivalent practical experience.
- 5 years of experience in Workflow Automation, Product Management, Software Engineering or Management Consulting (with Skill Development or Transformation experience).
- Experience working with Generative AI tools (e.g., LLMs, MCP) and prompt engineering.
- Experience turning manual workflows into functional code or automations.
- Experience driving adoption for technical products and managing the stakeholder ecosystems.
Preferred qualifications:
- Experience prototyping tools and exploring new technologies to evaluate their potential, independent of a formal software engineering role.
- Understanding of centralized governance models, with experience operationalizing programs across decentralized teams.
- Proficiency with SQL, including the ability to independently query adoption metrics and model business impact.
- Ability to simplify and communicate complex technical concepts (e.g., RAG, Vector, Agentic workflows) to non-technical leadership.
About the job
As an AI Solution Builder, you will act as a primary operator serving as the connective tissue between technical innovation, operational efficiency across our enablement and learning experience teams. You are an AI-fluent builder and strategist who will identify inefficiencies in the business, design agentic solutions to solve them, and manage the operational excellence required to scale them. You will operate at the intersection of strategy and software engineering, you will embed with delivery teams to uncover workflow bottlenecks and build cohesive, 0-to-1 production-grade AI automations. You are comfortable diving into messy, ambiguous business problems, translating them into technical specs, and shipping durable AI solutions that scale.Individual pay is determined by factors including job-related skills, experience, and relevant education or training.US: $152000 - $221000 (USD) + 15% bonus target + equity + benefits
Learn more about benefits at Google.
Responsibilities
- Embed with operational, content, and enablement teams to map workflows and identify bottlenecks for agentic automation.
- Build, test, and iterate on AI agents, custom tools, and automated pipelines using Gemini Enterprise, Antigravity, and frameworks like Agent Development Kit (ADK), LangChain, and Model Context Protocol (MCP).
- Transform proofs-of-concept into reliable, enterprise-grade internal applications with proper logging, observability, security controls, and human-in-the-loop checkpoints.
- Demystify AI tooling and drive adoption by authoring internal guides, architectural recipes, and best practices to help team members integrate automation into daily workflows.
- Establish evaluation frameworks to ensure AI safety and performance standards, quantify business impact, and strategically vet new AI opportunities for maximum return on investment.
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