AI Enablement Engineer

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.
• Reinforce practical adoption through examples, short demos, applied workflows, and direct support rather than relying only on broad show-and-tell sessions.

Own the feedback loop and continuous improvement motion

• Build and maintain a structured feedback loop with the engineering community to understand what is working, where friction exists, and which standards or workflows need refinement.
• Coordinate continuous improvement of AI standards and frameworks, not just their rollout, by channeling field feedback back to the leaders building them.
• Create mechanisms to gather lightweight input from engineers, team leads, and managers on adoption blockers, tooling gaps, and workflow quality.
• Help ensure the AIDLC working group stays connected to real engineering usage patterns, not just policy or infrastructure decisions.
• Translate recurring issues into clear recommendations for updates to playbooks, standards, templates, and training assets.

Create lightweight, high-frequency enablement content

• Produce bite-size recordings, short snippets, and practical reminders that keep engineers engaged with evolving AI standards and practices.
• Create concise do and do not guides, examples of strong AI-assisted workflows, review checklists, and quick-reference materials that reinforce expected engineering behaviors.
• Use AI tools such as Claude, ChatGPT, Gamma, and similar platforms to rapidly convert notes, strategy docs, working group outputs,…