Google
Linkedin · Posted 6d ago
AI Outcome Customer Engineer, Google Cloud
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Indexed description
In most instances, this position requires in-person interviews as part of the hiring process.Minimum qualifications:
- Bachelor’s degree or equivalent practical experience.
- 5 years of experience with cloud native architecture in a customer-facing or support role.
- Experience with technical delivery strategies and interfacing with product or engineering organizations.
- Experience in either system design or reading code (e.g., Java, C++, Python).
- Experience in system design or orchestration frameworks (e.g., LangGraph, AutoGen, CrewAI).
- Experience with enterprise integrations (APIs, enterprise content management (ECMs), identity), Cloud infrastructure, or AI/ML model deployments.
- 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.
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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