1 day ago
Tokyo, JapanSenior
Responsibilities
- Build, test, performance-tune, and ship production data and machine learning workloads with customers using PySpark, SQL, Python, BigQuery, Managed Spark, Spanner, and AlloyDB.
- Present working proofs using customer data while de-identifying sensitive information such as PCI and PHI.
- Build lakehouse architectures, metadata, semantic context, and access controls that provide trusted data foundations for agentic solutions.
- Train, evaluate, and deploy machine learning and agentic components with evaluation and monitoring tied to customer business metrics.
- Refactor and migrate analytics, Spark, and operational database workloads to Google Cloud engines and measure price-performance results.
- Embed with strategic customer accounts, provide white-glove deployment support, and communicate field insights to the product roadmap.
Requirements
- Bachelor’s degree in Engineering, Computer Science, a related field, or equivalent practical experience.
- At least 5 years of experience in software engineering, data engineering, solution architecture, machine learning, or technical consulting, including coding in Python and SQL.
- Experience with data cloud ecosystem products, technologies, or languages such as Apache Spark, PySpark, BigQuery, Spanner, Data Catalog, and AlloyDB.
- Experience taking production-grade data cloud solutions from conception to launch, architecting cloud data systems, and building structured and unstructured pipelines.
- Experience training, evaluating, and deploying machine learning models in production.
- Preferred experience implementing multi-agent systems with LangGraph, CrewAI, or ADK and patterns such as ReAct, self-reflection, and hierarchical delegation.
- Preferred experience with prompt development, model evaluation, AI applications over enterprise data, Gemini, agents, RAG, vector search, and LLM-native metrics.
- Preferred experience migrating analytics, Spark, or database workloads between platforms and optimizing state management and granular tracing.
Benefits
- The role is embedded directly with strategic customer accounts and offers access to Google Cloud’s AI portfolio, including Gemini models and the Vertex AI platform.
- The position provides collaboration with DeepMind engineering and research teams and opportunities to influence the Google Cloud product roadmap.
Tech Stack
Categories
Forward Deployed
About Google
Google builds consumer and enterprise software and services including Search, Android, YouTube, Chrome, Maps, Gmail, and Google Cloud. Its business model centers on digital advertising and paid cloud, software, and hardware offerings (e.g., Pixel and Nest) for consumers, developers, and organizations. Founded in 1998 and headquartered in Mountain View, California, Google operates globally as a subsidiary of Alphabet Inc.