AI-Native Engineering Enablement Lead

Role Description

Own the enterprise adoption of AI-native software engineering on the Replit platform. This is a platform enablement and transformation role, not a hands-on build role. You will drive adoption at scale, establish governance, build the operating model, and engage stakeholders from the business through to security and platform teams — treating Replit as the vehicle for an enterprise-wide change program. Success is measured by how broadly and safely the organization adopts AI-native engineering, not by code you personally write. You act as the connective tissue between business stakeholders, engineering, architecture, security, and platform teams.

Key Responsibilities

• Enterprise Adoption & Platform Strategy
  • 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.
  • Assess current delivery processes and target the highest-value adoption and automation opportunities.
• Governance & Operating Model
  • 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.
  • Ensure the operating model holds up in regulated/audited environments (Life Sciences a plus).
• Stakeholder Engagement & Change Management
  • 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.
• Consulting & Discovery
  • 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.
• Enablement
  • 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.

Qualifications

• Demonstrated track record leading enterprise platform adoption / developer-platform enablement programs (e.g., cloud platform, DevOps/DevEx, low-code, or a developer tooling rollout at scale).
• 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.

Requirements

• 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.
• Ability to hold a credible technical conversation and make sound architecture/security trade-off calls; deep individual coding is not the expectation.
• LLM fundamentals, prompt engineering, agentic workflows.
• AI governance and Responsible AI — especially controls for AI-generated code.

Experience Profile

• 8+ years in consulting, platform enablement, or technology transformation, with time spent leading enterprise-scale adoption/modernization initiatives.
• Prior experience in a similar enterprise platform enablement role (driving adoption, governance, stakeholder engagement, and platform strategy) is strongly preferred over a purely hands-on engineering background.
• Exposure to AI-assisted development platforms preferred.
• Life Sciences or other regulated-industry experience is a strong advantage.

Success Metrics

• Breadth and depth of enterprise adoption of AI-native engineering.
• Developer adoption and satisfaction.
• Governance and standards established and adhered to.
• Reduction in software delivery timelines and productivity improvements.
• Reusable AI-native development assets and playbooks created.
• Measurable business value delivered.