AI Outcome Customer Engineer, Google Cloud

On-site in London·Added today (last checked 15 Oct)

Google

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Overview

Skills

Requirements

Minimum qualifications:

Bachelor’s degree or equivalent practical experience.
7 years of experience with cloud native architecture in a customer-facing or support role.
Experience with technical delivery strategies and interfacing with product or engineering organizations.
Experience reading or debugging code in a general purpose coding language (e.g., Java, Python, JavaScript).
Experience in system design or orchestration frameworks (e.g., LangGraph, AutoGen, CrewAI).
Experience with enterprise integrations (e.g., APIs, enterprise content management (ECMs), identity), Cloud infrastructure, or AI/ML model deployments.

Preferred qualifications:

Experience diving deep into novel technical problems, deciphering ambiguity, diagnosing bugs, and emerging with credible architectural solutions.
Experience orchestrating specialized technical resources to execute complex builds.
Experience working with, presenting to, and influencing technical stakeholders or executive leaders.
Excellent executive communication skills, capable of translating deep technical integration issues into business impact.

The role

As an AI Outcome Customer Engineer in our GTM AI Tech organization, you work as an enterprise architect, technical debugger, engineering liaison and technical delivery manager. 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).It's an exciting time to join Google Cloud's Go-To-Market team, leading the AI revolution for businesses worldwide. You'll excel by leveraging Google's brand credibility-a legacy built on inventing foundational technologies and proven at scale. We'll provide you with the world's most advanced AI portfolio, including frontier Gemini models, and the complete Vertex AI platform, helping you to solve business problems. We're a collaborative culture providing direct access to DeepMind's engineering and research minds, empowering you to solve customer challenges. Join us to be the catalyst for our mission, drive customer success, and define the new cloud era-the market is yours.

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.
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.
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.

Hiring process

4 steps3 interviewsa task to do6–8 weeks in all

Apply online
Assessments
Online test · Not every role
You may be asked to take the Google Hiring Assessment (workstyle skills) and, for some roles, role-related assessments such as a coding exercise.
Recruiter conversations
With a recruiter · Phone call · 1–2 sessions
One or two shorter conversations assessing key skills, sharing information about the organisation and team, and asking about your preferences such as location.
Project work
In your own time · Not every role
For some roles, a small project before interviews: a case study, writing or code samples.
Interviews
With a rotating cast of Googlers
A panel of structured interviews, over video or in person, with a rotating cast of Googlers; open-ended role-related questions scored against the same rubric for every candidate, no brainteasers.
Decision and offer · With a recruiter

Structured interviews: every candidate for a role is scored against the same rubric; no brainteasers.
AI tools are not permitted during interviews.
For some roles, pre-selected interviews happen virtually before final interviews onsite.