What we're looking for

The Content Intelligence Management Lead will own the design, governance, and optimization of support content to power scalable AI, automation, and self-service experiences. This role sits at the intersection of customer experience, AI training, and technical content strategy—ensuring that structured, intelligent knowledge flows seamlessly across internal and external channels to reduce support effort and improve customer satisfaction.

Responsibilities

• Own the content lifecycle across FAQs, macros, internal knowledge bases, developer docs, user guides, and training assets - from creation to retirement.
• Develop structured content frameworks (e.g., modular templates, tagging, taxonomy) optimized for use in AI/LLM systems, chatbots, and search experiences.Govern knowledge freshness and accuracy through review workflows, SME input, performance analytics, and version control across multiple teams and regions.
• Enable AI and automation tools by preparing content for ingestion into generative AI models, prompt libraries, and search pipelines (e.g., retrieval-augmented generation).
• Design and implement a unified content system that supports multi-channel delivery (web, in-app, Slack, X/Twitter, GitHub), integrated with support tooling (Zendesk, Intercom, etc.).
• Collaborate with Engineering and Automation teams to build intelligent triage, answer engines, agent-assist suggestions, and escalation flows.Lead content contribution programs to scale knowledge development across CX, Product, and Support Engineering functions.

Establish metrics and dashboards

for content performance (views, deflection, effectiveness, freshness), using them to drive optimization efforts.

Qualifications

• Excellent English communication skills.
• 4+ years of experience in knowledge management, technical writing, or content strategy - preferably in a high-growth SaaS or developer-first environment.
• Experience designing and managing multi-format content across customer-facing and internal channels.
• Deep understanding of AI and LLM ecosystems (e.g., RAG pipelines, prompt engineering, vector stores), with experience prepping content for ingestion.
• Strong grasp of metadata and taxonomy design, including structured tagging and knowledge retrieval techniques.
• Familiarity with tools such as Zendesk, Confluence, Notion, GitBook, KCS methodology, and CMS platforms.
• Comfort working alongside AI, Engineering, and CX teams with a bias for action and iteration.
• Bonus: Experience with markdown documentation, REST API references, and developer documentation standards.