运维知识工程师

About the role

As a knowledge engineering role, you will transform behavioral and decision science backgrounds into structured operations experience, completing decision tree design, logic constraint definition, and knowledge standardization. You will connect expert experience, operations documentation, and intelligent agent reasoning pathways.

Key responsibilities

• Map typical fault paths, alert trigger conditions, handling branches, verification signals, and rollback conditions; organize post-incident review documents, ticket records, and expert interview content into knowledge entries, decision trees, and rule templates
• Assist in defining high-risk action boundaries, review checklists, and manual confirmation standards; participate in RAG knowledge base, Runbook structuring, and closed-loop handling constraint solution design
• Apply interview synthesis, process modeling, knowledge annotation, and post-incident analysis methods to complete SOP design, rule organization, and documentation and tabulation work
• Focus on knowledge organization, post-incident review, rule abstraction, and human-machine collaborative verification; transform implicit operations experience into annotatable, retrievable, constrained, and reusable research assets
• Collaborate with operations experts, algorithms, and platform teams to consolidate implicit operations experience into reusable knowledge assets supporting intelligent agent reasoning implementation

About you

• Bachelor's degree or above in Computer Science, Operations Engineering, Information Management, Systems Engineering, or related field
• Operations domain experience; familiar with fault handling, ticketing, and post-incident review processes; skilled at structuring and formalizing verbal experience and scenario cases
• Strong inductive reasoning, logical decomposition ability; good at communicating and collecting expert experience; outstanding documentation output capability
• Experience with Runbook, SOP, fault handling manuals, or operations knowledge base organization and construction (preferred)
• Understanding of decision tree modeling, process modeling, knowledge annotation; able to decompose conditions and clarify branches for complex business scenarios (preferred)
• Experience with operations expert interviews, requirement synthesis, and fault analysis; able to extract business rules and boundary constraints (preferred)
• Understanding of RAG, AIOps, intelligent agent products; understanding the value of knowledge bases for AI reasoning and fault handling (preferred)
• Experience demonstrating knowledge engineering, documentation architecture, or process organization projects (preferred)