{"id":3751815,"url":"https://alion.io/job/google-forward-deployed-engineer-v-genai-google-cloud-3","title":"Forward Deployed Engineer V, GenAI, Google Cloud","company":{"id":82,"name":"Google","domain":"google.com","url":"https://alion.io/company/google","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Career site","truth_index":{"grade":"B","score":78,"open_postings":134,"ghost_share":0.045,"stale_share":0.739,"repost_share":0.045,"time_to_fill_p50_days":30,"computed_at":"2026-11-07T05:47:08Z"}},"role":"Solutions","role_family":"Solutions","seniority":"senior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Singapore"],"countries":["SG"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":2,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"GCP","optional":false},{"name":"Gemini","optional":false},{"name":"Google BigQuery","optional":false},{"name":"Google Cloud Spanner","optional":false},{"name":"Machine Learning","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"Vertex AI","optional":false},{"name":"CrewAI","optional":true},{"name":"Google ADK","optional":true},{"name":"LangChain","optional":true},{"name":"LangGraph","optional":true},{"name":"LLM","optional":true},{"name":"Multi-Agent Systems","optional":true},{"name":"RAG","optional":true}],"status":"live","first_seen_at":"2026-11-04T14:11:20Z","employer_posted_date":"2026-11-06","last_verified_at":"2026-11-07T22:49:20Z","board_verified":true,"closed_at":null,"days_open":3,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":3},"description":"About the job\nGoogle Cloud’s Data Cloud team builds the data platform behind Google’s agentic Artificial Intelligence (AI) offerings: BigQuery, Managed Spark, borderless Lakehouse, Spanner, AlloyDB and Knowledge Catalog, integrated with Gemini Enterprise.As a Forward Deployed Engineer (FDE) on our Data Cloud team, you will be 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 customer's 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. You will 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.\nResponsibilities\nBuild 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.\nStand up working proofs with customer's data, including de-identifying sensitive data (e.g., Payment Card Information (PCI), Protected Health Information (PHI)).\nBuild the data foundation for agentic solutions, including lakehouse architectures, metadata and semantic context, and the access controls that let custom agents act on trusted data.\nTrain, evaluate and deploy machine learning and agentic components where they improve the customer’s business metric, with evaluation and monitoring in place.\nMigrate high-cost analytics, Spark and operational database workloads from other platforms onto Google Cloud engines, with measured price-performance results.\nQualifications\nMinimum qualifications:\nBachelor’s degree in Engineering, Computer Science, a related field, or equivalent practical experience.\n2 years of experience in software engineering, data engineering, solution architecture, machine learning, or technical consulting, including experience writing code in Python and SQL.\nExperience with data cloud ecosystem products, technologies, or languages (e.g., Apache Spark, PySpark, BigQuery, Spanner, Knowledge Catalog, AlloyDB).\nExperience taking production-grade data cloud solutions from conception to launch, including architecting data systems on cloud platforms (e.g., Google Cloud Platform (GCP)) and building structured/unstructured pipelines.\nExperience training, evaluating, and deploying machine learning models in production.\nPreferred qualifications:\nExperience implementing multi-agent systems using frameworks (e.g., LangGraph, CrewAI, Agent Development Kit (ADK)) and complex patterns (e.g., ReAct, self-reflection, hierarchical delegation).\nKnowledge of Large Language Model (LLM)-native metrics (e.g., tokens/sec, cost-per-request) and techniques for optimizing state management and granular tracing.\nExperience in prompt developing, model evaluation, and the creative application of AI.\nExperience building applications on large language models over enterprise data (e.g., Gemini, agents, Retrieval-Augmented Generation (RAG), vector search) and evaluating them for quality, latency and cost.\nExperience migrating analytics, Spark or database workloads between platforms with measured cost or performance 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