Forward Deployed Engineer V, GenAI, Google Cloud
Confirmed on the employer's own hiring board on Nov 4, 2026. First seen by Alion on Nov 4, 2026. Google scores B on the Alion truth index.
About the job
Google Cloud’s Data Cloud team builds the data platform behind Google’s agentic AI offerings: BigQuery, Managed Spark, borderless Lakehouse, Spanner, AlloyDB and Knowledge Catalog, integrated with Gemini Enterprise.
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, leveraging experience with programming languages (e.g. PySpark, SQL, Python) and the data cloud ecosystem (e.g. BigQuery, Managed Spark, Spanner, AlloyDB). You will build the data foundations that agents depend on, and train and evaluate machine learning models on customer data. By embedding with strategic accounts, you serve a dual purpose: providing "white glove" deployment of complex systems and acting as a critical 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.
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 (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.
Qualifications
Minimum 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.