CyberArk
AI Transformation Leader
Company
Role
AI Transformation Leader
Location
Job type
Full-time
Found on Mokaru
1 month ago
Salary
Job description
About the Role
This is a high-visibility, high-impact role at the intersection of AI strategy, engineering excellence, and organizational transformation. You will be the primary catalyst for accelerating AI adoption, improving engineering KPIs, enabling cutting-edge AI tooling, and serving as the key bridge between our India teams and the Global AI Center of Excellence.
If you are a hands-on technologist who thrives on building culture, coaching talent, and driving measurable change at scale — this role was built for you.
Key Responsibilities
Define and own the India site AI transformation roadmap aligned with company's global AI-First strategy, with clear OKRs, milestones, and quarterly reporting to senior leadership
Evaluate, pilot, and drive adoption of AI-powered developer tools (e.g., GitHub Copilot, Cursor, Claude Code, LLM-assisted code review, AI test generation) integrated into existing engineering workflows
Track and improve engineering KPIs including DORA metrics, AI adoption rates, developer productivity, and defect escape rates through data-driven insights and rapid experimentation
Design and deliver AI upskilling programs — workshops, hackathons, context engineering clinics, and structured learning curricula — across all engineering levels
Act as the primary India representative in Global AI COE forums, bringing global initiatives to local teams and amplifying India-led innovations to the global organization
Launch and lead innovation programs including AI hackathons, research sprints, and GenAI proof-of-concept incubators relevant to identity security and cybersecurity domains
Break down silos across engineering, product, and security teams by embedding AI-first thinking into planning, execution, and delivery
Build and chair an AI Transformation Council with representatives across engineering domains to sustain momentum and ownership across the site
Core Skills & Qualifications
12+ years in software engineering with 3+ years in a senior technical leadership, principal, or staff-level role
Demonstrable hands-on experience with GenAI tools and LLM APIs (OpenAI, Anthropic, Azure OpenAI) and AI-assisted development workflows
Strong grounding in software engineering quality practices: CI/CD, DORA metrics, test automation, and code review culture
Proven experience driving large-scale technology adoption and change management programs across 100+ engineers
Excellent communication and executive stakeholder management skills — comfortable presenting to VP and C-level leadership
Strong coaching and mentoring track record with genuine passion for engineering talent development
Experience operating in a global matrixed organization with cross-timezone collaboration
Data-driven mindset — skilled at defining, instrumenting, and tracking engineering and AI adoption KPIs
#LI-HK01
Preferred Qualifications
Experience building or leading an AI Center of Excellence, AI Guild, or internal AI community of practice
Published thought leadership: conference talks, blog posts, patents, or open-source contributions in AI/ML or developer productivity
Advocacy for diversity in tech, particularly Women in Technology and early-career talent programs
Familiarity with cloud-native AI/ML platforms (AWS Bedrock, AWS SageMaker, Azure ML, Google Vertex AI)
Hands-on exposure to agentic AI frameworks and retrieval-augmented generation (RAG) pipelines
Background in cybersecurity, identity & access management, or enterprise security product development
Ideal Candidate Profile
You are a practitioner first — you lead by doing, not just directing. You are a natural bridge-builder who earns credibility with both engineers and executives. You simplify complexity without losing depth, and you anchor every initiative to measurable outcomes. Most importantly, you are energized by building culture — you see complacency as a solvable problem and treat every engineer as a talent worth investing in.
You thrive in ambiguity, move fast, and leave teams more capable than you found them.


