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Socure
New York, United States; San Francisco, California, United States; Seattle, Washington, United States
Source: Socure careers · View original posting
From Socure's posting. “We” and “our” refer to the employer.
Why Socure?
Socure is building the identity trust infrastructure for the digital economy — verifying 100% of good identities in real time and stopping fraud before it starts. The mission is big, the problems are complex, and the impact is felt by businesses, governments, and millions of people every day.
We hire people who want that level of responsibility. People who move fast, think critically, act like owners, and care deeply about solving customer problems with precision. If you want predictability or narrow scope, this won’t be your place. If you want to help build the future of identity with a team that holds a high bar for itself — keep reading.
We are looking for an AI Enablement Engineer to accelerate AI adoption across engineering by turning standards, tooling patterns, and proven workflows into repeatable day-to-day practice. This role sits at the intersection of AI tooling enablement, developer productivity, engineering standards, lightweight training, feedback loops, and technical storytelling.
You will serve as the connective tissue between the leaders defining AI foundations and standards and the broader engineering organization that needs to adopt them. This is not a passive communications role. It is a hands-on, builder-oriented role focused on driving a more standardized AI Development Lifecycle (AIDLC) across engineering.
You will help evangelize the standards and frameworks being built by engineering leaders, coordinate the continuous improvement of those standards based on feedback from engineers in the field, and create the lightweight systems that keep the organization engaged and improving over time.
AI adoption becomes broader and less dependent on a small set of self-driven power users.
The engineering organization has a more visible and standardized AIDLC process with clear expectations and reusable patterns.
Engineers receive lightweight, frequent guidance that helps translate standards into day-to-day practice.
Feedback from the field results in continuous improvement of AI frameworks, templates, and best practices.
Leaders can point to clear metrics and stories showing how AI tooling adoption is improving speed, quality, and consistency across engineering.
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