مهندس تحقيق نتائج الذكاء الاصطناعي، الهندسة الميدانية

As an AI Outcome Customer Engineer, Forward Deployed Engineering, 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).

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.
  
• 5 years of experience troubleshooting technical issues for internal/external partners or customers.
• Experience with technical delivery strategies and interfacing with product or engineering organizations.
• 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.

Preferred qualifications:

• Experience interfacing directly with core product/engineering organizations regarding infrastructure or AI/ML model deployments, managing technical delivery strategies, and debugging systems.