Staff Forward Deployed Engineer, GenAI, Google Cloud
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Minimum qualifications:
- Bachelor’s degree in Engineering, Computer Science, a related field, or equivalent practical experience.
- 8 years of experience building and shipping production-grade AI-driven solutions to external or internal customers using Python, Typescript or comparable languages.
- Experience leading technical discovery sessions with business stakeholders and engineering teams to define AI and hardware infrastructure requirements.
- Experience designing and building AI systems on cloud platforms (e.g., Google Cloud Platform (GCP)).
- Experience building pipelines for structured, unstructured data, incorporating vector databases and retrieval-augmented generation retrieval-augmented generation (RAG)-like architectures to power enterprise-grade AI solutions.
Preferred qualifications:
- Master’s degree or PhD in AI, Computer Science, or a related technical field.
- Experience implementing multi-agent systems using frameworks (e.g., LangGraph, CrewAI, or Google’s Agent Development Kit (ADK)) and patterns like ReAct, self-reflection, and hierarchical delegation.
- Knowledge of large language model native metrics (tokens/sec, cost-per-request) and techniques for optimizing state management and granular tracing.
About the job
As a GenAI Forward Deployed Engineer at Google Cloud, you will be an embedded builder bridging the gap between frontier AI products and production-grade reality for our customers. You will function as a builder-consultant, moving beyond high-level architecture to code, debug, and jointly ship bespoke agentic solutions directly within the customer’s environment.Responsibilities
- Serve as the lead developer for AI applications, transitioning from rapid prototypes to production-grade agentic workflows (e.g., multi-agent systems, model context protocol servers) that drive measurable return on investment.
- Architect and code the connective tissue between Google’s AI products and customer's live infrastructure, including APIs, legacy data silos, and security perimeters.
- Build high-performance evaluation pipelines and observability frameworks to ensure agentic systems meet rigorous 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 product feature requests for the Engineering teams.
- Drive engineering excellence by mentoring talent, co-building with customer teams, and influencing cross-functional strategies to uplevel organizational technical capabilities.
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