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Socure

AI Enablement Engineer

Hub - New York (Remote)$210k–$230kfulltimemidAdded 2 days ago

About this role

Socure seeks an AI Enablement Engineer to drive standardized AI adoption across the engineering organization by creating tooling, frameworks, and practical workflows that make AI development repeatable. You'll bridge leadership's AI standards with day-to-day engineering practice through hands-on enablement, feedback loops, and lightweight training content.

What you'll do

  • Evangelize AI tooling patterns, standards, and best practices defined by engineering leaders and working groups
  • Build structured feedback mechanisms with engineering teams to identify adoption friction and workflow gaps
  • Create bite-size enablement content including recordings, guides, checklists, and examples using AI tools
  • Coordinate continuous improvement of AI standards and frameworks based on field feedback
  • Define and track adoption metrics and dashboards measuring AIDLC usage and productivity impact
  • Support hands-on enablement through office hours, team sessions, and direct engineering support

What they're looking for

  • AI/LLM tools proficiency (Claude, ChatGPT, Gamma)
  • Technical writing and documentation
  • Process design and standardization
  • Data analysis and metrics definition
  • Cross-functional collaboration and stakeholder management
  • Training and enablement program development
  • Software engineering fundamentals
  • Change management and adoption strategy
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Socure

Socure builds identity verification and document verification solutions using backend services, APIs, and machine learning capabilities. The company is hiring software engineers, frontend developers, quality engineers, technical support specialists, and growth engineers to enhance their platform's reliability, user experience, and customer acquisition.

Website
socure.com
View all jobs at Socure

Likely interview questions

  • Describe a time you drove adoption of a new tool or standard across an engineering team—what worked and what didn't?
  • How would you approach gathering feedback from distributed engineering teams on workflow friction with AI standards?