Staff Forward Deployed Engineer, GenAI, Google Cloud

Google Germany GmbH; Hamburg; Vor Ort; EUR 70.000 - 90.000; Vollzeit

Zusammenfassung

IT-Systemhaus der Bundesagentur für Arbeit is seeking a Cloud-Architect to bridge the gap between frontier AI products and production-grade reality. The role involves managing blockers to production, architecting solutions, and transforming insights into future product roadmaps. Candidates should have a Bachelor’s degree and 8 years of experience in building AI solutions using languages like Python and Typescript. This position is located in Hamburg with opportunities for preferred working locations in various cities, including Berlin and Munich.

Qualifikationen

• 8 years of experience building AI-driven solutions.
• Experience leading technical discovery sessions.
• Experience designing AI systems on cloud platforms.

Aufgaben

• Bridge the gap between AI products and production-grade reality.
• Manage blockers to production, solving integration complexities.
• Architect and code connective tissue between AI products and customer infrastructure.

Kenntnisse

Python

Typescript

AI systems design

Cloud platforms

Data pipelines

Ausbildung

Bachelor’s degree in Engineering, Computer Science, or a related field

Master’s degree or PhD in AI, Computer Science, or a related field

Tools

Google Cloud Platform (GCP)

Jobbeschreibung

Beginn

• Cloud-Architect

Note: By applying to this position you will have an opportunity to share your preferred working location from the following:Zürich, Switzerland; Vienna, Austria; Berlin, Germany; Hamburg, Germany; Munich, Germany.

• Bachelor’s degree in Engineering, Computer Science, a related field, or equivalent practical experience.
• 8 years of experience building and shipping production‑grade AI‑driven solutions to external or internal customers using Python, Typescript or comparable languages.
• Experience leading technical discovery sessions with business stakeholders and engineering teams to define AI and hardware infrastructure requirements.
• Experience designing and building AI systems on cloud platforms (e.g., Google Cloud Platform (GCP)).
• Experience building pipelines for structured, unstructured data, incorporating vector databases and retrieval‑augmented generation retrieval‑augmented generation (RAG)‑like architectures to power enterprise‑grade AI solutions.

Preferred qualifications

• Master’s degree or PhD in AI, Computer Science, or a related technical field.
• Experience implementing multi‑agent systems using frameworks (e.g., LangGraph, CrewAI, or Google’s Agent Development Kit (ADK)) and patterns like ReAct, self‑reflection, and hierarchical delegation.
• Knowledge of large language model native metrics (tokens/sec, cost‑per‑request) and techniques for optimizing state management and granular tracing.

About the Job

As a GenAI Forward Deployed Engineer at Google Cloud, you will be an embedded builder bridging the gap between frontier AI products and production‑grade reality for our customers. You will function as a builder‑consultant, moving beyond high‑level architecture to code, debug, and jointly ship bespoke agentic solutions directly within the customer’s environment.

In this role, you will manage blockers to production including solving the integration complexities, data readiness issues, and state‑management issues that prevent AI from reaching enterprise‑grade maturity. By embedding with accounts, you will serve a dual purpose: providing white‑glove deployment of AI systems and acting as a critical feedback loop, transforming real‑world field insights into Google Cloud’s future product roadmap.

• Architect and code the connective tissue between Google’s AI products and customer’s live infrastructure, including APIs, legacy data silos, and security perimeters.
• Build high‑performance evaluation pipelines and observability frameworks to ensure agentic systems meet rigorous requirements for accuracy, safety, and latency.
• Identify repeatable field patterns and technical friction points in Google’s AI stack, converting them into reusable modules or product feature requests for the Engineering teams.
• Drive engineering excellence by mentoring talent, co‑building with customer teams, and influencing cross‑functional strategies to uplevel organizational technical capabilities.