AI Outcome Customer Engineer, Forward Deployed Engineering, Google Cloud

Preferred qualifications:

• Experience into novel technical problems, deciphering ambiguity, diagnosing bugs, and emerging with credible architectural solutions.
• Experience orchestrating specialized technical resources to execute complex builds.
• Experience engaging with, presenting to, and influencing technical stakeholders or executive leaders.
• Ability to communicate in German fluently to support clients in this region.
• Excellent executive communication skills, capable of translating deep technical integration issues into business impact.

About the job

As an AI Outcome Customer Engineer in our go-to-market (GTM) AI Tech organization, you will bridge the gap between pre-sales agreement shaping and post-sales execution. Entering the agreement cycle for AI accounts, you will ensure solutions are shaped strictly through the lens of adoption, rapid activation, and viable delivery, aligning with our Forward Deployed Engineering (FDE) organization.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. Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

Germany: €122000 - €125000 (EUR) + 15% bonus target + equity + benefits

Learn more about benefits at Google.

Responsibilities

• Partner with account teams and practice Customer Engineers (CEs) during technical evaluation phases to assess project feasibility, shape proposals for long-term adoption, and validate FDE engagement requests.
• Lead upfront technical design for enterprise-grade AI solutions, ensuring seamless and secure integration of models, agents, and connectors into existing customer data pipelines, identity providers, and compliance boundaries.
• Dive into code-level context to diagnose and resolve complex customer implementation issues, identify core product bugs, and test workarounds to clear execution roadblocks.
• Serve as the definitive liaison to core Product and Engineering teams, troubleshooting systemic deployment blockers and translating real-world field feedback into actionable feature requests.
• Steer implementation strategy through technical authority and architectural foresight while owning the technical reality of delivery alongside customer-facing teams.

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Source: Statistisches Bundesamt (Destatis), table; dl-de/by-2-0; StarIn's calculation