Forward Deployed Engineer V, GenAI, Cloud

About the Role

Google Cloud’s Data Cloud team builds the data platform behind Google’s agentic AI offerings. As a Forward Deployed Engineer (FDE) on the Data Cloud team, you are an embedded builder who bridges the gap between frontier data solutions and production-grade reality for customers. You will function as an "innovator-builder," working directly within customers’ environments to move data and machine learning workloads from idea to production on Google Cloud.

Key Responsibilities

• 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.
• 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.

Requirements & Tech Stack

• Python
• SQL
• PySpark
• BigQuery
• Managed Spark
• Spanner
• AlloyDB
• Google Cloud
• Machine Learning
• Data Cloud Ecosystem Products/Technologies/Languages
• Apache Spark
• Data Catalog

Qualifications

• 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.