Engineering AI Transformation Director

Ping Identity•San Francisco, CA

About The Position

Ping Identity is seeking a visionary and execution-oriented Director of AI Transformation within Engineering to lead the evolution of its Engineering and R&D organization. This role is dedicated to re-engineering how engineers work, think, and deliver value. As the architect of the internal AI evolution, the Director will be responsible for defining and executing a roadmap that integrates AI into every facet of the R&D lifecycle—from ideation and coding to testing, deployment, and cultural mindset. This role sits at the intersection of Engineering, Operations, and Change Management to ensure the R&D engine remains world-class in the age of intelligence.

Requirements

10+ years in Engineering Leadership, DevOps Transformation, or Technical Program Management.
Proven experience leading large-scale organizational change is a must.
Deep understanding of the modern SDLC, cloud-native architecture, and the current landscape of LLMs/Generative AI tools for developers.
Expert-level knowledge of Agile methodologies and how they must evolve to accommodate AI-driven workflows.
Exceptional communication skills with the ability to influence C-suite stakeholders and win the "hearts and minds" of cynical engineers.

Responsibilities

Define and execute the "AI-First" R&D roadmap to transition from manual to augmented engineering.
Design an operating model that balances rapid AI experimentation with security and governance.
Partner with the Chief Development Officer to ensure AI transformation scales alongside business growth.
Integrate GenAI tools (e.g., Copilot, AI agents) into every stage of the SDLC.
Overhaul workflows for code reviews, testing, and documentation using LLM-driven automation.
Standardize AI-assisted development practices to ensure consistency across all engineering squads.
Lead organizational change to foster a culture of AI-driven continuous improvement.
Launch internal training and institutionalize prompt engineering and AI literacy.
Identify and mitigate barriers to AI tool adoption within the developer community.
Define the "AI ROI" using metrics like Developer Velocity, Quality of Service, and Cycle Time.
Deliver data-driven insights to leadership regarding the efficacy and impact of AI investments.
Continuously refine processes based on performance data and developer sentiment.