Description

Key responsibilities

•        Define and operationalize the AI engineering and delivery process flow, including tools, standards, release practices, and lifecycle controls.

•        Build and lead the delivery side of the organization, covering development and operations as two core pillars.

•        Establish baseline KPIs for Engineering and DevSecOps functions.

•        Evaluate and bring in ADLC and AI in SDLC practices with partners and select suitable vendor and opensource products to build the future stack that will drive KPI improvements

•        Partner closely with enterprise architecture leadership to align engineering tooling and implementation choices with enterprise standards.

•        Collaborate with CIO, CISO, CFO, risk, and legal stakeholders to ensure delivery practices align with enterprise-wide AI governance expectations.

•        Stand up pod-based or scrum-team-based operating structures to deliver priority AI use cases while refining the broader framework.

•        Drive fast execution, iterative learning, and measurable outcomes instead of long planning cycles without delivery

. Role requirements

•        Demonstrated experience building greenfield AI ecosystems, not only isolated agents or point AI use cases.

•        Experience with creating internal developer platforms and drive adoption of it,

•        Strong hands-on leadership style, with willingness to work on-site and operate at a fast pace in close partnership with internal leadership.

•        Deep familiarity with DevOps, AI Ops, LLM Ops, and industrialized AI delivery methods.

•        Ability to define process, select tools, coach teams, and adjust rapidly based on observed bottlenecks.

•        Strong communication skills with the ability to operate at both executive and engineering depth.

Role Descriptions: 

AI-led DevSecOps Leader Skills: Digital : Cloud DevOps~AI for Leadership Experience Required: 10 & Above

Key Skills

Education

Bachelor's degree