Job description

As a Generative AI Forward Deployed Engineer (FDE) at Google Cloud, you will be an embedded innovator-builder moving beyond high-level architecture to code, debug, and jointly ship bespoke, scalable agentic solutions directly with strategic partners inside customer environments. In this role, you will bridge the gap between frontier AI prototypes and production-grade reality, resolving integration complexities, data readiness challenges, and state-management bottlenecks that prevent systems from reaching enterprise maturity. You will also provide white-glove deployments and establish a critical feedback loop to Google, transforming field insights into future product roadmap enhancements alongside partner engineering teams.It's an exciting time to join Google Cloud’s Go-To-Market team, leading the AI revolution for businesses worldwide. You’ll excel by leveraging Google's brand credibility—a legacy built on inventing foundational technologies and proven at scale. We’ll provide you with the world's most advanced AI portfolio, including frontier Gemini models, and the complete Vertex AI platform, helping you to solve business problems. We’re a collaborative culture providing direct access to DeepMind's engineering and research minds, empowering you to solve customer challenges. Join us to be the catalyst for our mission, drive customer success, and define the new cloud era—the market is yours. Individual pay is determined by factors including job-related skills, experience, and relevant education or training. US: $174000 - $252000 (USD) + 15% bonus target + equity + benefits Learn more about benefits at Google . • S erve as a tech lead and developer with AI partners for complex AI applications, working with partner teams to transition from rapid prototypes to production-grade, replicable agentic workflows (e.g., multi-agent systems, MCP servers) that drive measurable Return on Investment (ROI). • Build high-performance evaluation pipelines and observability frameworks to ensure partner developed agentic systems meet requirements for accuracy, safety, and latency. • Identify repeatable partner and 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 a AI partner’s forward deployed engineering team to instill Google-grade development best practices. • Help partners to build their own agentic delivery capabilities to set them up for long term success, focusing on the ROI at customer engagements ensuring customer activation. • Bachelor’s degree in Engineering, Computer Science, a related field, or equivalent practical experience. • 5 years of experience with software development using Python or similar coding languages. • Experience architecting AI systems on cloud platforms (e.g., Google Cloud Platform (GCP)). • Experience taking production-grade AI-driven solutions from conception to launch for customers. • Experience guiding technical discovery sessions with customers. • Experience building pipelines for structured and unstructured data using both vector databases and RAG-like architectures to power enterprise AI solutions. Minimum qualifications: • Bachelor’s degree in Engineering, Computer Science, a related field, or equivalent practical experience. • 5 years of experience with software development using Python or similar coding languages. • Experience architecting AI systems on cloud platforms (e.g., Google Cloud Platform (GCP)). • Experience taking production-grade AI-driven solutions from conception to launch for customers. • Experience guiding technical discovery sessions with customers. • Experience building pipelines for structured and unstructured data using both vector databases and RAG-like architectures to power enterprise 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, 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.

Requirements

Must-haves

Learn more about benefits at Google .
Build high-performance evaluation pipelines and observability frameworks to ensure partner developed agentic systems meet requirements for accuracy, safety, and latency.
Identify repeatable partner and 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 a AI partner’s forward deployed engineering team to instill Google-grade development best practices.
Help partners to build their own agentic delivery capabilities to set them up for long term success, focusing on the ROI at customer engagements ensuring customer activation.
Bachelor’s degree in Engineering, Computer Science, a related field, or equivalent practical experience.
5 years of experience with software development using Python or similar coding languages.
Experience architecting AI systems on cloud platforms (e.g., Google Cloud Platform (GCP)).
Experience taking production-grade AI-driven solutions from conception to launch for customers.
Experience guiding technical discovery sessions with customers.
Experience building pipelines for structured and unstructured data using both vector databases and RAG-like architectures to power enterprise AI solutions.
Bachelor’s degree in Engineering, Computer Science, a related field, or equivalent practical experience.
5 years of experience with software development using Python or similar coding languages.
Experience architecting AI systems on cloud platforms (e.g., Google Cloud Platform (GCP)).
Experience taking production-grade AI-driven solutions from conception to launch for customers.
Experience guiding technical discovery sessions with customers.
Experience building pipelines for structured and unstructured data using both vector databases and RAG-like architectures to power enterprise AI solutions.

Nice-to-haves

US: $174000 - $252000 (USD) + 15% bonus target + equity + benefits
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.

Application prep

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Keywords to mirror

aigopythongcpreactllm

Platform: google
Effort: medium