From Google's careers page
Status: listed on the employer's own careers page when we last checked (daily). Confirm on their site before applying.
This role is designed for high-agency engineers with a founder’s mindset.
As a GenAI Forward Deployed Engineer (FDE) at Google Cloud, you are an embedded builder who bridges the gap between frontier AI products and production-grade reality within customers. Unlike traditional advisory roles, you function as an "innovator-builder," moving beyond high-level architecture to code, debug, and jointly ship bespoke agentic solutions directly within the customer’s environment. This role is designed for high-agency engineers with a founder’s mindset.
You will address blockers to production including solving the integration complexities, data readiness issues, and state-management challenges that prevent AI from reaching enterprise-grade maturity. By embedding with strategic accounts, you serve a dual purpose: providing "white glove" deployment of complex AI systems and acting as a critical feedback loop, transforming real-world field insights into Google Cloud’s future product roadmap.
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.
Architect and develop reusable generative media solutions, vertical-specific templates, and a library of technical assets (code repositories, cookbooks) to accelerate adoption.
Serve as a developer for complex AI applications, transitioning from rapid prototypes to production-grade agentic workflows (e.g. multi-agent systems, MCP servers) 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 Engineering teams.
Bachelor’s degree in Engineering, Computer Science, a related field, or equivalent practical experience.
8 years of experience in software development or machine learning.
3 years of experience building with or fine-tuning generative AI models for image generation, video generation, or audio generation.
Experience taking production-grade AI-driven solutions from conception to launch for customers, and architecting AI systems on cloud platforms (e.g., GCP).
Experience building pipelines for structured and unstructured data using both vector databases and RAG-like architectures to power enterprise AI solutions.
Experience with software development in one or more programming languages (e.g., Python) for generative media
Master’s degree or PhD in AI, Computer Science, or a related technical field.
Experience with the architecture of generative models (e.g., Diffusion Models, Vision Transformers).
Experience building applications or proof-of-concepts using Python and ML libraries (e.g., PyTorch, TensorFlow, JAX).
Experience implementing multi-agent systems using frameworks (e.g., LangGraph, CrewAI, ADK) and complex patterns (e.g., ReAct, self-reflection, hierarchical delegation).
Experience managing Go-To-Market strategies for AI technologies.
Knowledge of LLM-native metrics (e.g., tokens/sec, cost-per-request) and techniques for optimizing state management and granular tracing.
Bachelor’s degree in Engineering, Computer Science, a related field, or equivalent practical experience.
8 years of experience in software development or machine learning.
3 years of experience building with or fine-tuning generative AI models for image generation, video generation, or audio generation.
Experience taking production-grade AI-driven solutions from conception to launch for customers, and architecting AI systems on cloud platforms (e.g., GCP).
Experience building pipelines for structured and unstructured data using both vector databases and RAG-like architectures to power enterprise AI solutions.
Experience with software development in one or more programming languages (e.g., Python) for generative media
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