דרושים » תוכנה » content strategy & ai enablement specialist

we are the global leader in identity security, trusted by organizations around the world to secure human and machine identities in the modern enterprise. our ai-powered identity security platform applies intelligent privilege controls to every identity with continuous threat prevention, detection and response across the identity lifecycle. with identity security, organizations can reduce operational and security risks by enabling zero trust and least privilege with complete visibility, empowering all users and identities, including workforce, it, developers and machines, to securely access any resource, located anywhere, from everywhere. job description
as a content strategy & ai enablement specialist, you will not own a single products documentation lifecycle. instead, you will act as a systems architect for the entire organization. your mission is to transform documentation from a collection of static, feature-based articles into a modular, workflow-driven knowledge system that is optimized for both human consumption and ai retrieval (rag).
you will be responsible for building the "content factory" of the future-designing the frameworks, taxonomies, and governed ai workflows that enable delivery pods to produce high-impact, outcome-based documentation at scale.
key responsibilities
1. outcome-based strategy & framework design
design and implement a universal framework for outcome-based documentation that aligns with customer personas and real-world journeys. develop modular content models (the "lego" approach) that allow documentation components to be reused across different products and integration patterns. harmonize cross-product integration documentation to ensure a seamless end-to-end User Experience. 2. ai enablement & governance
design and operationalize governed ai documentation workflows that assist writers in drafting, flagging inconsistencies. develop a knowledge grounding strategy (rag) to ensure ai-generated responses (e.g., via ai agents) are high-accuracy and contextually relevant. lead efforts in terminology alignment and taxonomy management to improve ai retrieval precision. 3. operational rigor & impact measurement
establish a robust measurement framework to track the impact of documentation on support ticket deflection. define and monitor key performance indicators (kpis) such as flow completion rates, case-to-doc ratios. collaborate with support, customer success, and product management to create a field-to-doc feedback loop that proactively identifies content gaps. 4. cross-functional leadership
act as a consultant to product-based delivery pods, providing them with the templates and tools needed to transform content into structured, ai-ready assets. establish processes for continuous improvement, measurement, and optimization of content performance for ai agents.