AI Outcome Customer Engineer, Forward Deployed Engineering

Google Google · Big Tech · Chicago, IL +2

This role focuses on enterprise architecture, technical debugging, and engineering liaison for AI solutions, bridging pre-sales and post-sales execution. The Customer Engineer will ensure solutions are shaped for adoption, rapid activation, and viable delivery, acting as an Enterprise Architect and Technical Delivery Manager. Responsibilities include technical design, debugging, product/engineering integration, and aligning forward-deployed engineering. The role involves architecting integration into customer IT ecosystems and driving technical delivery strategy, leveraging Google's AI portfolio like Gemini models and Vertex AI platform.

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

  • 10 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 architecture
  • technical debugging
  • engineering liaison
  • technical design
  • systemic debugging
  • Product and Engineering integration
  • strategic Forward Deployed Engineering (FDE) alignment
  • customer's IT ecosystem
  • technical delivery strategy
  • technical evaluation phases
  • long-term adoption
  • enterprise-grade AI solutions
  • seamless and secure integration
  • customer data pipelines
  • identity providers
  • compliance boundaries
  • code-level context
  • complex customer implementation issues
  • core product bugs
  • execution roadblocks
  • definitive liaison
  • core Product and Engineering teams
  • systemic deployment blockers
  • real-world field feedback
  • actionable feature requests
  • technical authority
  • architectural foresight
  • technical reality of delivery

Other signals

  • enterprise architecture
  • technical debugging
  • engineering liaison
  • customer integration
  • Vertex AI platform
  • Gemini models