Forward Deployed Engineer V, GenAI, Google Cloud
Google · Posted Nov 7
Search engine, cloud computing, software, hardware, and artificial intelligence services
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About the role
Google Cloud's Data Cloud team builds the data platform behind Google's agentic AI offerings, including BigQuery, Managed Spark, a borderless Lakehouse, Spanner, AlloyDB and Knowledge Catalog, integrated with Gemini Enterprise. As a Forward Deployed Engineer on this team, you embed directly within strategic customer environments to move data and machine learning workloads from idea to production on Google Cloud. The role combines hands-on building of data foundations and agentic solutions with feeding real-world field insights back into Google Cloud's product roadmap.
What you will do
- Build and ship production data and machine learning workloads with customers, writing, testing, and performance-tuning PySpark, SQL and Python on BigQuery, Managed Spark, Spanner and AlloyDB.
- Present working proofs with customers’ data, including de-identifying sensitive data (e.g., PCI, PHI).
- Build the data foundation for agentic solutions, including lakehouse architectures, metadata, semantic context, and the access controls that let custom agents act on trusted data.
- Train, evaluate and deploy machine learning and agentic components where they improve the customer’s business metric, with evaluation and monitoring in place.
- Refactor and migrate high-cost analytics, Spark and operational database workloads from other platforms onto Google Cloud engines, with measured price-performance results.
Skills used in this role
What the employer is looking for
- Bachelor’s degree in Engineering, Computer Science, a related field, or equivalent practical experience.
- 2 years of experience in software engineering, data engineering, solution architecture, machine learning, or technical consulting, including coding experience in Python and SQL.
- Experience with data cloud ecosystem products, technologies, or languages (e.g., Apache Spark, PySpark, BigQuery, Spanner, Data Catalog, and AlloyDB).
- Experience taking production-grade data cloud solutions from conception to launch, including architecting data systems on cloud platforms (e.g., GCP) and building structured/unstructured pipelines.
- Experience training, evaluating, and deploying machine learning models in production.
Preferred qualifications
- Experience implementing multi-agent systems using frameworks (e.g., LangGraph, CrewAI, ADK) and complex patterns (e.g., ReAct, self-reflection, hierarchical delegation).
- Experience in prompt developing, model evaluation, and the creative application of AI.
- Experience building applications on large language models (LLM) over enterprise data (e.g., Gemini, agents, RAG, vector search) and evaluating them for quality, latency and cost.
- Experience migrating analytics, Spark or database workloads between platforms with measured cost or performance outcomes.
- Knowledge of LLM-native metrics (e.g., tokens/sec, cost-per-request) and techniques for optimizing state management and granular tracing.
Benefits and support
- Compensation and benefits are detailed in the job posting
About Google
Google LLC is an American multinational technology company focusing on artificial intelligence, online advertising, search engine technology, cloud computing, computer software, quantum computing, e-commerce, and consumer electronics. As a subsidiary of Alphabet Inc., it is widely recognized for services like Google Search, YouTube, Android, and the Gemini AI ecosystem.
- Industry
- Internet
- Company size
- 180000+ employees
- Founded
- 1998-09-04
- Location
- Mountain View, California, USA
- Funding stage
- Public Company
Funding
Public Company · $25M raised
- 1999-06-07Series A$25M
Leadership
Chief Executive Officer
Senior Vice President and Chief Financial Officer
Senior Vice President and Chief Business Officer
Chief Executive Officer, Google Cloud
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