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.
- 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.