AI Enablement Engineer

Remote: yes Posted: 2026-09-09

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.

Drive AI adoption and standardization across engineering

- Serve as the primary enablement partner for AI foundations, standards, and framework adoption across the engineering organization.

- Evangelize the AI tooling patterns, standards, and best practices being defined by engineering leaders and working groups.

- Help move the organization toward a standardized AIDLC process by making expected behaviors, workflows, and quality bars explicit and easy to adopt.

- Partner with leaders across platform, engineering, and operations to identify where AI should become part of default engineering practice rather than an optional side project.