Forward Deployed Engineer, Generative AI, Google Cloud (Japanese, English)

In this role, you will be an embedded builder who bridges the gap between frontier Artificial Intelligence (AI) products and production-grade reality within customers. You will manage blockers to production including solving the integration issues, data readiness issues, and state-management tests that prevent AI from reaching enterprise-grade maturity. You will be providing deployment of AI systems and act as a 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.

Minimum qualifications
Bachelor's degree in Science, Technology, Engineering, Mathematics, or equivalent practical experience.
5 years of experience in Python and a related machine learning package (e.g., Keras, PyTorch, HF Transformers).
Experience in applied AI, with building systems around pre-trained models (e.g., prompt engineering, fine-tuning, Retrieval-Augmented Generation (RAG), orchestrating model interactions with external tools to deliver solutions).
Experience with architecting, deploying, or managing solutions on a Cloud Platform (e.g., Google Cloud Platform).
Ability to communicate in Japanese and English fluently to interact with internal and external stakeholders.

Preferred qualifications
Master’s degree or PhD in AI, Computer Science, or a related technical field.
Experience with 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 (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 Artificial Intelligence (AI) applications, transitioning from prototypes to production-grade agentic workflows (e.g., multi-agent systems, Model Context Protocol (MCP) servers) that drive Return on Investment (ROI).
Architect and code the connection between Google’s AI products and customer's live infrastructure, including Application Programming Interfaces (APIs), legacy data silos, and security perimeters as part of a team.
Build evaluation pipelines and observability frameworks to ensure agentic systems meet 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 project success and end-user adoption.