领域
Machine Learning & AI
技能档案
此技能定义了各角色和级别的期望。
角色数
1
包含此技能的角色
级别数
5
结构化成长路径
必要要求
0
其余 5 个可选
Machine Learning & AI
LLM & Generative AI
2026/2/22
选择当前级别并对比期望。下方卡片显示晋升所需掌握的内容。
表格展示从初级到首席的技能深度变化。点击行查看详情。
| 角色 | 必要性 | 描述 |
|---|---|---|
| LLM Engineer | Knows LLM safety basics: jailbreaking, prompt injection, harmful content. Understands basic approaches to content filtering and safety classifiers for LLM applications. |
| 角色 | 必要性 | 描述 |
|---|---|---|
| LLM Engineer | Independently implements safety pipelines: input/output filtering, guardrails (NeMo Guardrails, Guardrails AI), red-teaming. Configures content moderation and PII detection for production LLM. |
| 角色 | 必要性 | 描述 |
|---|---|---|
| LLM Engineer | Designs comprehensive safety systems: multi-layer defense, adversarial robustness testing, dynamic safety policies. Develops custom safety classifiers and automated red-teaming frameworks. |
| 角色 | 必要性 | 描述 |
|---|---|---|
| LLM Engineer | Defines LLM safety standards for the team. Establishes safety testing protocols, incident response for safety events, governance processes. Coordinates safety R&D and compliance. |
| 角色 | 必要性 | 描述 |
|---|---|---|
| LLM Engineer | Shapes enterprise LLM safety strategy. Defines safety governance, compliance with AI regulations, and organization-wide safety standards. Mentors leads on advanced safety techniques and risk management. |