AI Outcome Customer Engineer, Forward Deployed Engineering

Google Google · Big Tech · New York, NY +2

This role is for an AI Outcome Customer Engineer within Google Cloud's Forward Deployed Engineering team. The primary focus is on bridging the gap between pre-sales and post-sales execution for strategic enterprise accounts, acting as an enterprise architect, technical debugger, and engineering liaison. The role involves shaping solutions for adoption and delivery, leading technical design, debugging complex issues, integrating AI models and agents into customer IT ecosystems, and translating field feedback into product improvements. The goal is to drive customer success and adoption of Google's AI portfolio, including Gemini models and Vertex AI.

What you'd actually do

  1. Partner with Account teams and practice Customer Engineer (CEs) during technical evaluation phases to assess project feasibility, shape proposals for long-term adoption, and validate FDE engagement requests.
  2. 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.
  3. Dive into code-level context to diagnose and resolve complex customer implementation issues, identify core product bugs, and test workarounds to clear execution roadblocks.
  4. 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.
  5. Steer implementation strategy through technical authority and architectural foresight while owning the technical reality of delivery alongside customer-facing teams.

Skills

Required

  • 7 years of experience troubleshooting technical issues for internal/external partners or customers.
  • Experience in either system design or reading code (e.g., Java, C++, Python).

Nice to have

  • Experience with enterprise integrations (APIs, enterprise content management (ECMs), identity), Cloud infrastructure, and AI/ML model deployments.
  • Ability to do in-depth search into novel technical problems, decipher extreme ambiguity, diagnose bugs, and emerge with credible architectural solutions.
  • Excellent executive communication skills, capable of translating deep technical integration issues into business impact.

What the JD emphasized

  • enterprise architect
  • technical debugger
  • engineering liaison
  • technical design
  • systemic debugging
  • Product and Engineering integration
  • technical delivery strategy
  • customer challenges
  • technical authority
  • architectural foresight
  • technical reality of delivery