AI Outcome Engineer, Forward Deployed Engineering, Google Cloud
Role Overview
The AI Outcome Customer Engineer bridges pre-sales agreement shaping and post-sales execution for AI accounts.
The role shapes solutions around adoption, rapid activation, and viable delivery.
The engineer architects how technical assets integrate into customer IT ecosystems, considering connectors, identity, data residency, and legal constraints.
Key Skills for This Role
Full Job Posting
Location and work authorization
- Applicants may indicate Doha, Qatar or Riyadh, Saudi Arabia as their preferred working location.
- Applicants must have work authorization in Saudi Arabia; visa sponsorship is not available.
- In most instances, interviews for this position are in person.
Minimum qualifications
- Bachelor’s degree or equivalent practical experience.
- Five years of experience troubleshooting technical issues for internal or external partners or customers.
- Experience with technical delivery strategies and interfacing with product or engineering organizations.
- Experience with system design or orchestration frameworks, such as LangGraph, AutoGen, or CrewAI.
- Experience with enterprise integrations, cloud infrastructure, or AI/ML model deployments.
Preferred qualifications
- Direct product or engineering interface experience related to infrastructure or AI/ML deployments, delivery strategy management, and systems debugging is preferred.
About the role
The AI Outcome Customer Engineer bridges pre-sales agreement shaping and post-sales execution for AI accounts.
The role shapes solutions around adoption, rapid activation, and viable delivery.
The engineer architects how technical assets integrate into customer IT ecosystems, considering connectors, identity, data residency, and legal constraints.
Responsibilities
- Partner with account teams and practice customer engineers to assess feasibility, shape adoption-focused proposals, and validate FDE engagement requests.
- Diagnose complex customer implementation issues, identify product bugs, and test workarounds.
- Troubleshoot systemic deployment blockers with product and engineering teams and translate field feedback into feature requests.
- Guide implementation strategy and own technical delivery alongside customer-facing teams.
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