Field Solutions Architect GenAI
Job Overview
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Job Description
Overview
The Field Solutions Architect GenAI at Google Cloud bridges the gap between frontier AI products and production-grade reality. In this role, you will code, debug, and deploy bespoke agentic solutions directly within the customer environment, ensuring high performance and security.
Minimum Qualifications
- Bachelor’s degree in Science, Technology, Engineering, Mathematics or equivalent.
- 8 years of experience in production-grade AI solutions with L400-level Python.
- Expertise in technical discovery with executive stakeholders and engineering teams.
- Experience building full-stack solutions that interface with enterprise systems.
Preferred Qualifications
- Master's degree or PhD in Computer Science, AI, Machine Learning or related field.
- Experience with multi-agent systems using frameworks like LangGraph, CrewAI, or Google’s ADK.
- Knowledge of LLM-native metrics and state optimization techniques.
- Ability to implement secure agentic workflows with MCP, tool-calling, and OAuth authentication.
About The Job
The Google Cloud Platform team empowers customers to digitally transform their business using enterprise-grade solutions from infrastructure to hardware. As a Field Solutions Architect GenAI, you serve as an embedded builder consulting on AI integrations, deployment, and optimization for clients around the world.
Responsibilities
- Lead development of AI applications from prototypes to production-grade workflows.
- Architect and code the integration between Google’s AI products and customer infrastructure.
- Build evaluation pipelines and observability frameworks ensuring accuracy and latency.
- Identify repeatable patterns and friction points to drive product improvements.
Key skills/competency
- AI Solutions
- Python
- Cloud Architecture
- Full-Stack Development
- Multi-Agent Systems
- API Integration
- Data Management
- Security
- Observability
- Executive Communication
How to Get Hired at Google
- Customize your resume: Highlight cloud and AI project experience.
- Research Google: Understand their Cloud products and culture.
- Prepare technical demos: Showcase full-stack AI integrations.
- Practice executive communication: Refine your stakeholder interactions.
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