Forward Deployed Engineer, GenAI, Google Cloud (English, Japanese)
Summary
Builds and deploys production-grade generative AI solutions for enterprise customers, integrating Google Cloud’s AI stack with client systems and translating technical needs into scalable agentic workflows.
- Serve as a developer for AI applications, transitioning from rapid prototypes to production-grade agentic workflows (e.g., multi-agent systems, Model Context Protocol (MCP) servers) that drive measurable Return on Investment (ROI).
- Architect and code the connective tissue between Google’s AI products and customer's live infrastructure, including Application Programming Interfaces (APIs), legacy data silos, and security perimeters as part of an expert 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 long-term project success and high end-user adoption. Translate technical concepts to non-technical and executive Japanese-speaking audiences.
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 taking production-grade AI solutions from conception to launch and architecting AI systems on cloud platforms (e.g., Google Cloud Platform).
- Experience building pipelines for structured and unstructured data using vector databases and Retrieval-Augmented Generation (RAG)-like architectures to power enterprise AI solutions.
- Experience managing technical discovery sessions.
- Ability to communicate in Japanese and English fluently to manage local stakeholders.
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, Agent Development Kit (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.