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About 7Vals
7Vals is a collective of passionate innovators and builders, applying best practices to ensure awesomeness. We build disruptive enterprise software products. We’re now developing the next generation of products for IT Departments across IT Asset Management, IT Service Management, Security & Patch Management, and CMDB combining strong technical foundations with modern automation and AI capabilities.
Role Overview
We’re looking for an AI Content Engineer to build and scale an AI-native content engine that positions 7Vals as a trusted authority for IT leaders across modern discovery platforms.
This role focuses on designing and operating a content production system powered by AI one that consistently produces high-quality, practitioner-grade content optimized for visibility across LLMs (ChatGPT, Claude, Gemini, etc.), search engines, and AI-generated answers.
You will not just create content you will build the system that creates content. This includes designing pipelines, prompt frameworks, evaluation loops, and scalable workflows that enable high-quality output at speed.
You’ll work closely with marketing, product, and content teams to ensure that everything produced reflects real-world IT use cases across ITAM, ITSM, Patch Management, and CMDB and stands out in an era where generic content is ignored by both users and AI systems.
This is an ideal role for someone who thinks in systems, understands AI deeply, and cares about producing content that is technically credible, discoverable, and impactful.
Who You Are
Systems-first builder: Thinks in workflows, pipelines, and scalable systems. Designs repeatable processes instead of one-off outputs.
AI-native operator: Deeply familiar with LLMs and how they behave. Understands prompting, evaluation, failure modes, and how to improve output quality systematically.
Technically grounded: Has exposure to IT environments or enterprise software and can distinguish between surface-level and practitioner-grade content.
Editorially sharp: Strong writing instincts with the ability to identify high-quality, credible content versus generic AI-generated output.
Quality-obsessed: Maintains high standards and actively identifies and eliminates low-quality or “sloppy” outputs from systems and workflows.
Outcome-driven: Focused on measurable visibility LLM citations, search presence, and inbound demand not just content volume.
Key Responsibilities
Build AI-Native Content Systems Design, develop, and maintain scalable content production pipelines—from research and ideation to drafting, editing, publishing, and performance tracking.
Create High-Impact Content at Scale Produce and/or orchestrate the production of high-quality, practitioner-grade content that addresses real IT challenges and ranks across both search engines and LLM-generated responses.
Optimize for LLM & Search Visibility Continuously refine content structures, formats, and strategies to improve visibility in AI-generated answers, search rankings, and knowledge retrieval systems.
Own Content Architecture & Taxonomy Develop and manage topic clusters, internal linking strategies, schema, and content taxonomy to strengthen authority and discoverability.
Build Prompting & Evaluation Frameworks Create reusable prompt libraries, evaluation loops, and quality control mechanisms to ensure consistent, high-quality AI outputs.
Collaborate Across Teams Partner with content creators, product teams, and marketing to translate insights, product knowledge, and domain expertise into scalable content assets.
Measure, Learn, and Iterate Track performance metrics such as LLM citation rates, AI visibility, search rankings, and engagement. Continuously improve systems based on data and feedback.
Required Skills & Qualifications
Nice to Have
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