GenAI Forward Deployed Engineer — Cloud AI Solutions

Google

United States

On-site

USD 207,000 - 300,000

Full time

14 days+

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Job summary

Google Cloud is seeking a Forward Deployed Engineer IV specializing in GenAI to bridge frontier AI products with customer environments. You’ll move beyond architecture to code, debugging, and shipping bespoke agentic solutions within real client systems.

You will embed with strategic accounts to deploy white-glove AI systems, shaping Google Cloud’s product roadmap with field insights.

Qualifications

Bachelor's degree in Engineering, Computer Science, a related field, or equivalent practical experience.
8 years of experience with software development using Python or similar coding languages.
Experience building pipelines for structured and unstructured data using both vector databases and RAG-like architectures to power enterprise AI solutions.
Experience taking production-grade AI-driven solutions from conception to launch for customers.
Experience architecting AI systems on cloud platforms (e.g. Google Cloud Platform (GCP)).
Experience leading technical discovery sessions with customers.
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, ADK) and complex patterns (e.g., ReAct, self-reflection, 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.

Responsibilities

Serve as a developer for complex AI applications, transitioning from rapid prototypes to production-grade agentic workflows that drive measurable ROI.
Architect and code the connective tissue between Google’s AI products and customer's live infrastructure, including APIs, legacy data silos, and security perimeters as part of an expert team.
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 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 long-term project success and high end-user adoption.