AI Outcome Customer Engineer, Google Cloud

About the job

As an AI Outcome Customer Engineer in our GTM AI Tech organization, you work as an enterprise architect, technical debugger, engineering liaison and technical delivery manager. You will bridge the gap between pre-sales agreement shaping and post-sales execution. Entering the agreement cycle during the technical evaluation phase for AI accounts, you will ensure that solutions are shaped strictly through the lens of adoption, rapid activation, and viable delivery. Aligned with our Forward Deployed Engineering (FDE) organization, you will architect how these technical assets actually integrate into the customer's IT ecosystem (connectors, identity, data residency, legal constraints).

Google welcomes people with disabilities.

Minimum qualifications:

• Bachelor’s degree or equivalent practical experience.
  
• 7 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 reading or debugging code in a general purpose coding language (e.g., Java, Python, JavaScript).
• Experience in system design or orchestration frameworks (e.g., LangGraph, AutoGen, CrewAI).
• Experience with enterprise integrations (e.g., APIs, enterprise content management (ECMs), identity), Cloud infrastructure, or AI/ML model deployments.

Preferred qualifications:

• Experience diving deep into novel technical problems, deciphering ambiguity, diagnosing bugs, and emerging with credible architectural solutions.
• Experience orchestrating specialized technical resources to execute complex builds.
• Experience working with, presenting to, and influencing technical stakeholders or executive leaders.
• 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.