R&D Director, GenAI Enablement at Cellebrite

Petah Tikva, Center District, Israel -

Full Time

Start Date

Immediate

Expiry Date

06 Sep, 26

Salary

0.0

Posted On

08 Jun, 26

Experience

10 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

AI Transformation, Agentic SDLC, Engineering Leadership, CI/CD, Cloud Computing, AWS, GitHub Copilot, Claude Code, Cursor, KPI Definition, Organizational Transformation, Governance, Risk Management, Compliance, Systems Thinking, Software Engineering

Industry

Public Safety

Description

Cellebrite’s (Nasdaq: CLBT) mission is to enable its global customers to protect and save lives by enhancing digital investigations and intelligence gathering to accelerate justice in communities around the world. Cellebrite’s AI-powered Digital Investigation Platform enables customers to lawfully access, collect, analyze and share digital evidence in legally sanctioned investigations while preserving data privacy. Thousands of public safety organizations, intelligence agencies and businesses rely on Cellebrite’s digital forensic and investigative solutions—available via cloud, on-premises and hybrid deployments—to close cases faster and safeguard communities. To learn more, visit us at www.cellebrite.com, https://investors.cellebrite.com/investors and find us on social media @Cellebrite Cellebrite is seeking a Director of AI Enablement to lead a large-scale transformation of a 300+ R&D organization into an AI-first, agentic software development lifecycle (SDLC). This role is responsible for redefining how engineering teams operate by embedding AI coding agents across the full SDLC-design, development, testing, documentation, review, and operations. The goal is to shift AI from a productivity layer into core development infrastructure, driving measurable improvements in velocity, quality, and delivery. This is a system-level leadership role focused on operating models, engineering practices, and large-scale organizational transformation. Responsibilities: Lead the design and rollout of an AI-first, agentic SDLC across large R&D teams Redefine development practices with AI agents as first-class contributors across the SDLC Drive adoption of AI coding agents across CI/CD, testing, and developer workflows Establish standards for human–AI collaboration, including governance, guardrails, and quality gates Build scalable enablement frameworks to transition entire teams to AI-first ways of working Define role evolution (engineers as orchestrators, reviewers, and AI collaborators) Create reusable architectures, patterns, and agent blueprints Define and track KPIs (velocity, quality, cycle time, test coverage, etc.) Lead pilot teams and scale validated AI workflows across the organization Partner with engineering leadership to embed AI-first practices into planning and delivery Requirements Proven experience leading large-scale AI transformation in engineering organizations (100+ engineers) Experience moving teams to structured, agent-based or AI-augmented SDLC Hands-on experience with AI coding agents in real development workflows Strong understanding of AI integration across the SDLC (design, coding, testing, documentation, review) Strong software engineering background (10+ years, CI/CD, testing, cloud environments such as AWS) Experience with AI coding tools (e.g., GitHub Copilot, Claude Code, Cursor) Ability to define organizational standards and best practices for AI-driven development Experience leading large-scale organizational transformation in distributed R&D environments Proven ability to drive executive alignment and long-term strategy Director/VP-level leadership experience in a product company Experience defining KPIs for engineering performance and transformation Strong understanding of governance, risk, and compliance in AI-driven development Systems thinker with a view of AI as a foundational layer of the development stack Personal Characteristics null

Responsibilities

Lead the transformation of a 300+ person R&D organization into an AI-first development environment by embedding AI coding agents across the full SDLC. Establish standards for human-AI collaboration and define KPIs to measure improvements in velocity and quality.