Forward Deployed Engineer, Gen AI, Google Cloud (English, Japanese)
Summary
Forward Deployed Engineer at Google Cloud in Tokyo who embeds with customers to take GenAI prototypes (Gemini, Vertex AI) into production—building agentic workflows, integrations, and evaluation/observability pipelines in Python, and feeding field insights back into Google's AI roadmap. Requires fluent Japanese and English.
- 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.
Minimum qualifications:
- Bachelor's degree in Science, Technology, Engineering, Mathematics, or equivalent practical experience.
- 2 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 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.