Replit Consultant with Pharma experience.

Replit Consultant with Pharma experience.

Location: NJ - Hybrid role.

Contract for 12 months

AI-Native Engineering Enablement Lead Replit

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 Replitas 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.

enterprise platform adoption, governance design, stakeholder alignment, operating-model and roadmap ownership, change management, enablement and mentoring.

Is not: a senior developer or solutions architect whose primary output is production code. Technical fluency is required to earn credibility and make sound decisions but the day-to-day work isdriving people, process, and platform strategy.

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.

Required Experience & Skills

Enablement & Transformation (primary this is what we're screening for)

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.

Technical Fluency (enough to lead credibly not to build)

Solid grasp of the modern software engineering lifecycle, Git, CI/CD, APIs & microservices, and cloud platforms (AWS/Azure/Google Cloud Platform).

Working familiarity with AI-assisted development tools Replit, GitHub Copilot, Cursor, and similar.

You should be able to hold a credible technical conversation and make sound architecture/security trade-off calls; deep individual coding is not the expectation.

AI Knowledge

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.