AI Outcome Customer Engineer, Forward Deployed Engineering

Google Google · Big Tech · Sunnyvale, CA +2

This role is for an AI Outcome Customer Engineer focused on enterprise AI solutions. The engineer will act as an enterprise architect and technical debugger, bridging pre-sales and post-sales execution. Responsibilities include shaping solutions for adoption, leading technical design for integration of models and agents, debugging complex implementation issues, and serving as a liaison to product and engineering teams. The role involves ensuring seamless integration into customer IT ecosystems, including connectors, identity, and data residency, and driving technical delivery strategy. The position emphasizes solving business problems with Google's AI portfolio 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

  • Bachelor’s degree or equivalent practical experience.
  • 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 dive deep 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
  • integration
  • deployment
  • technical design
  • systemic debugging
  • Product and Engineering integration
  • strategic Forward Deployed Engineering (FDE) alignment
  • technical delivery strategy
  • customer challenges
  • 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
  • systemic deployment blockers
  • real-world field feedback
  • actionable feature requests
  • technical authority
  • architectural foresight
  • technical reality of delivery
  • customer-facing teams
  • troubleshooting technical issues
  • system design
  • reading code
  • enterprise integrations
  • Cloud infrastructure
  • AI/ML model deployments
  • novel technical problems
  • extreme ambiguity
  • diagnose bugs
  • architectural solutions
  • executive communication skills
  • deep technical integration issues
  • business impact

Other signals

  • customer-facing
  • technical debugger
  • enterprise architect
  • integration
  • deployment