The Google Cloud Platform team helps customers transform and build what's next for their business — all with technology built in the cloud. Our products are developed for security, reliability and scalability, running the full stack from infrastructure to applications to devices and hardware. Our teams are dedicated to helping our customers — developers, small and large businesses, educational institutions and government agencies — see the benefits of our technology come to life. As part of an entrepreneurial team in this rapidly growing business, you will play a key role in understanding the needs of our customers and help shape the future of businesses of all sizes use technology to connect with customers, employees and partners.
In this role, you will be an embedded builder who bridges the gap between frontier AI products and production-grade reality within customers. You will manage blocker to production including solving the integration complexities, data readiness issues, and state-management tests that prevent AI from reaching enterprise-grade maturity. You will provide deployment of AI systems and act as a feedback loop, transforming field insights into Google Cloud’s future product roadmap.
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
- Serve as the lead developer for AI applications, transitioning from prototypes to production-grade agentic workflows (e.g., multi-agent systems, MCP servers) that drive return on investment.
- Architect and code the connections between Google’s AI products and customers' live infrastructure, including APIs, legacy data silos, and security perimeters.
- Build evaluation pipelines and observability frameworks to ensure agentic systems meet requirements for accuracy, safety, and latency.
- Identify repeatable field patterns and technical friction points in Google’s AI stack, converting them into reusable modules or formal product feature requests for the engineering teams.
- Co-build with customer engineering teams to instill Google-grade development best practices, ensuring project success and end-user adoption.
Qualifications
Minimum qualifications:
- Bachelor’s degree in a Science, Technology, Engineering, Mathematics, or related field, or equivalent practical experience.
- 3 years of experience in providing production-grade AI solutions to external or internal customers with L400-level in Python, and architecting AI systems on cloud platforms.
- Experience in developing generative AI (GenAI) solutions with foundation models, first-party model tuning, and advanced retrieval-augmented generation (RAG) architectures.
Preferred qualifications:
- Master’s degree or PhD in Artificial Intelligence, Computer Science, or a related technical field.
- Experience in implementing multi-agent systems using frameworks (e.g., LangGraph, CrewAI, or Google’s ADK) and patterns like ReAct, self-reflection, and hierarchical delegation.
- Knowledge of large language model (LLM)-native metrics (e.g., tokens/sec, cost-per-request) and techniques for optimizing state management and granular tracing.
- Ability to implement agentic workflows incorporating MCP, tool-calling, and OAuth-based authentication.
- Ability to build full-stack applications that interact with enterprise IT infrastructures, and perform interviews to find the business problem and translate hardware/AI constraints for technical teams.