AI-Native Engineering Enablement Lead

Xlysi

Posted 24d ago

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Own and drive enterprise-wide adoption of AI-native engineering on Replit
Define the platform adoption strategy across functions, product teams, and business units
Build AI-native engineering roadmaps aligned to business priorities and secure leadership buy-in
Design the governance model for AI-generated code — review gates, quality bars, IP/security controls, auditability
Define the AI-native SDLC, engineering standards, and reusable development patterns
Establish developer workflows and guardrails on the Replit platform
Partner with Product Owners, Engineering Managers, Enterprise Architecture, Security, and Infrastructure to align on adoption and controls
Present recommendations and progress to senior leadership; manage expectations and secure sponsorship
Lead organizational change management — overcome resistance, build champions, sustain momentum
Run discovery workshops with business and engineering teams
Identify and prioritize candidate applications for AI-assisted modernization by business value and feasibility
Build business cases and ROI assessments to justify and sequence investment
Design and deliver training, best-practice guidance, and reusable playbooks
Mentor and coach engineering teams through the adoption curve
Create reusable AI-native development assets that scale across teams

8+ years in consulting, platform enablement, or technology transformation
Track record leading enterprise platform adoption / developer-platform enablement programs
Proven ability to drive adoption, governance, and change management across large, cross-functional organizations
Strong stakeholder management up to senior/executive level
Digital transformation, business process analysis, business-case/ROI development
Enterprise architecture awareness and Agile delivery
Solid grasp of the modern software engineering lifecycle, Git, CI/CD, APIs & microservices, and cloud platforms (AWS/Azure/GCP)
Working familiarity with AI-assisted development tools — Replit, GitHub Copilot, Cursor, and similar
LLM fundamentals, prompt engineering, agentic workflows
AI governance and Responsible AI — especially controls for AI-generated code

Skills