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As an AI Outcome Customer Engineer within the Forward Deployed Engineering (FDE) organization, you will bridge the gap between pre-sales agreement shaping and post-sales execution. You will enter the agreement cycle during the technical evaluation phase for AI accounts, ensuring that solutions are designed with a focus on adoption, rapid activation, and viable delivery. Your role will involve architecting how technical assets integrate into the customer's IT ecosystem, taking into account connectors, identity, data residency, and legal constraints.
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.
- 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.
Minimum qualifications:
- Bachelor’s degree or equivalent practical experience.
- 7 years of experience troubleshooting technical issues for internal/external partners or customers.
- Experience with enterprise integrations (APIs, enterprise content management (ECMs), identity), Cloud infrastructure, or AI/ML model deployments.
- Experience with technical delivery strategies and interfacing with product or engineering organizations.
- Experience in system design or orchestration frameworks (e.g., LangGraph, AutoGen, CrewAI).
- Ability to speak and write in Arabic and English fluently.
Preferred qualifications:
- Experience interfacing directly with core product/engineering organizations regarding infrastructure or AI/ML model deployments, managing technical delivery strategies, and debugging systems.
