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. ABOUT THE ROLE 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. WHAT YOU’LL DO 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, meeting recordings, and examples into reusable enablement assets. - Build repeatable prompt patterns, templates, and content-generation workflows that can be reused by engineering leaders and the broader org. - Support office hours, hack sessions, team-specific enablement sessions, or time-zone-aware rollouts where hands-on guidance is needed. Measure adoption and tell the story of progress - Partner with engineering leads to define dashboards and metrics that track AIDLC adoption, AI tool usage patterns, and productivity impact over time. - Contribute to technical storytelling on two tracks: how AI standards and frameworks are evolving, and how the engineering org is trending toward more standardized AIDLC behavior. - Use metrics to create clear, data-backed narratives about progress, gaps, and opportunities for further adoption. - Help package adoption metrics and success stories for leadership updates, engineering all-hands, planning discussions, and quarterly reviews. - Support the visibility of practical wins such as reduced delivery time, stronger code quality guardrails, faster new-hire productivity, and better reuse of AI-assisted workflows. Support onboarding and engineering learning where it matters - Contribute to engineering onboarding by ensuring new engineers are introduced to approved AI tools, standard workflows, and expected usage patterns early in their ramp. - Create or refresh targeted onboarding assets related to AI tooling, engineering standards, and how teams are expected to work with agents and copilots. - Support role-specific learning where needed, but keep the primary focus on AI adoption and engineering enablement rather than owning onboarding as the central charter. WHAT SUCCESS LOOKS LIKE - 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. WHAT YOU BRING - 5+ years of experience in developer productivity, developer experience, technical enablement, engineering communications, technical program leadership, or a similar role in a fast-moving technical organization. - Strong understanding of how modern engineering teams work, including software development lifecycles, code review, testing, developer tooling, platform/reliability concepts, and adoption of engineering standards. - Strong experience using AI tools like Claude, ChatGPT, Gamma, Cursor, and related platforms to create content, package workflows, summarize information, and build reusable assets quickly from existing source material. - Demonstrated ability to create reusable AI-assisted workflows, including prompt libraries, short-form enablement content, templates, and playbooks. - Ability to turn evolving technical standards into clear, practical, high-frequency enablement for engineers and managers. - Strong written and verbal communication skills, with the ability to translate complex technical ideas into concise, useful guidance for different audiences. - Experience building feedback loops, adoption mechanisms, or learning systems that help organizations change behavior at scale. - Comfort partnering across engineering leadership, platform teams, operations, recruiting, and business stakeholders. - High ownership, strong judgment, attention to detail, and bias for action in ambiguous environments NICE TO HAVE - Experience in B2B SaaS, platform engineering, identity/fraud/risk, fintech, or other technically complex environments. - Familiarity with engineering productivity metrics, usage analytics, cost governance, or dashboarding related to AI tooling. - Experience running office hours, technical communities, guilds,…