About the job
The Information Engineering team at NetApp creates and maintains technical documentation published on docs.netapp.com, the company’s most visited web site. Our team of strategic thinkers and creative problem-solvers owns NetApp’s technical documentation infrastructure as well as pipelines that integrate technical content into products, sites, and AI tools.
Responsibilities
Partner with content standards leads, UX designers, engineers, product managers, and data scientists to identify high-value opportunities for AI in the technical content lifecycle.
Design, build, launch and iterate agent-driven automated workflows that gather, normalize, and optimize source content for publishing and downstream reuse.
Build and operationalize AI-assisted semantic enrichment pipelines (ontology, metadata, and taxonomy tagging) to improve content findability, consistency, and performance.
Develop agents and checks that enforce content standards and ensure knowledge is optimized for integration in AI solutions.
Implement measurement and reporting systems to track adherence to legal, brand, style, inclusivity, accessibility, accuracy, freshness, and structural standards.
Define governance patterns (guardrails, evaluations, and human-in-the-loop review triggers) to ensure responsible, reliable AI outcomes.
Use signals (quality metrics, evaluation results, stakeholder feedback) to continuously improve agent behavior, prompt/context strategies, and workflow performance.
Qualifications
Minimum
Hands-on experience building and improving AI agents and/or multi-agent orchestrations for complex technical content.
Experience integrating generative AI capabilities into enterprise workflows (design through production).
Experience implementing governance mechanisms such as guardrails, validation checks, evaluations, and human review triggers in AI-assisted workflows.
Working knowledge of GenAI fundamentals and responsible AI practices (risk identification, governance, and monitoring).
Ability to translate editorial/quality standards into machine-readable rules, automated checks, and agent behaviors.
Strong cross-functional collaboration skills and the ability to turn business needs into clear requirements and workable technical solutions.
Experience with GitHub and modern CI/CD automation (for example, GitHub Actions).
Experience developing and implementing ontology, metadata, and taxonomy for large content sets.
Experience with content curation, knowledge management, and information architecture concepts.
Familiarity with documentation tooling and formats such as Jekyll, AsciiDoctor, and Markdown.
Experience with GitHub Copilot and AI-assisted development practices in day-to-day engineering work.
Experience with test, verification, and evaluation of tooling for AI systems.
Preferred
Demonstrated ability to learn new skills quickly (for example, context engineering, programming languages, or new platforms) and apply them to deliver measurable outcomes.