AI Adoption Engineer - East
$275,000–$325,000 year
Remote
Job Summary
Own all four adoption phases for enterprise accounts, from post-deployment kickoff through renewal signature. Maintain a weekly customer-specific 30/60/90 roadmap and run every meeting type, including kickoffs, enablement sessions, bi-weekly feedback calls, power-user 1:1s, weekly syncs, exec/leadership syncs, and Quarterly Business Reviews. Publish monthly async leadership updates to account executives based on real usage data. Own customer-specific integrations, custom prompt engineering, and edge-case debugging; instrument accuracy tracking and collaborate with customers to ensure credible datasets. Identify failure modes during 1:1s, translate them into roadmap items, and debug platform issues. Push for service-launch integration by wiring Traversal into customer OAR/compliance checklists. Synthesize feedback into structured product input and contribute to the cross-account Slack community.
Required Qualifications
- 5+ years in a technical customer-facing role: solutions engineering, solutions architecture, technical account management, or customer success engineering at an enterprise software or infrastructure company
- Genuine technical depth — you can read logs, understand distributed systems failures, and have a point of view on why an AI agent produced a wrong root cause
- Demonstrated ability to run executive-level conversations and engineer-level conversations in the same week with the same customer
- Experience driving adoption of a technical product with a complex, multi-stakeholder customer — not just managing the relationship, but actively changing how people work
- Strong written communication
- Comfort with ambiguity and a builder's instinct
Desired Qualifications
- Background in observability, AIOps, ITSM, or incident management — you understand the workflows Traversal lives inside
- Experience as an SRE or FDE
- Experience with Slack-based enterprise workflows, ServiceNow, or Splunk as a practitioner or integrator
- Familiarity with consumption-based or credit-based SaaS pricing models and what they mean for customer behavior
- Prior experience at a company that sold to infrastructure, platform, or SRE teams at large enterprises
- Exposure to prompt engineering or AI product configuration — you don't need to be an ML engineer, but you should understand why a system prompt matters
- History of turning a skeptical power user into a champion — you have a story about this
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