AI-Native Engineering Enablement Lead — Replit | NJ - Hybrid role. | W2 Only.

Role Summary

Lead enterprise adoption of AI-native software engineering on the Replit platform.

This is a platform enablement and transformation role, not a hands-on development role. Focus on adoption, governance, operating model, stakeholder management, change management, and enablement.

Key Responsibilities

• Drive enterprise-wide adoption of Replit and AI-native engineering.

• Define adoption strategy, roadmap, governance, and operating model.

• Establish standards, guardrails, quality controls, security, IP, and auditability for AI-generated code.

• Partner with Engineering, Product, Architecture, Security, Infrastructure, and senior leadership.

• Lead change management and build internal AI-native engineering champions.

• Run discovery workshops and identify high-value AI modernization opportunities.

• Develop business cases and ROI assessments.

• Create training, playbooks, best practices, and reusable AI-native development assets.

• Mentor engineering teams through the adoption process.

Required Skills & Experience

• 8+ years in consulting, platform enablement, developer platforms, or technology transformation.

• Proven experience driving enterprise platform adoption, governance, and change management.

• Strong senior/executive stakeholder management skills.

• Experience with digital transformation, business process analysis, and ROI/business cases.

• Understanding of enterprise architecture and Agile delivery.

• Strong knowledge of Git, CI/CD, APIs, microservices, and AWS/Azure/GCP.

• Familiarity with Replit, GitHub Copilot, Cursor, or similar AI-assisted development tools.

• Understanding of LLMs, prompt engineering, agentic workflows, AI governance, and Responsible AI.

• Technical fluency to make architecture and security decisions, but deep coding is not required.

• Life Sciences or other regulated-industry experience is a strong plus.

Success Metrics

• Enterprise adoption and developer satisfaction.

• Governance and standards established and followed.

• Improved software delivery speed and productivity.

• Reusable AI-native development assets and playbooks.

• Measurable business value.