Design a Skills inference system

Design an end-to-end ML system to power a "Skills" feature for a professional social network.

The product wants to:

• Extract and infer a member’s skills from profile text, resume/CV uploads, job titles/descriptions, projects, and possibly user behavior.
• Normalize skills to a canonical taxonomy (e.g., map "PyTorch" vs "pytorch" vs "torch" to the same skill).
• Optionally recommend missing skills to add.

Requirements

• High precision for visible skills; avoid embarrassing incorrect skills.
• Support near-real-time updates when a user edits their profile or uploads a new resume.
• Must scale to tens/hundreds of millions of members.
• Consider privacy/security for resume parsing.

What to cover

• Data sources and labeling strategy
• Taxonomy/ontology and normalization
• Model approach(es) and features
• Training pipeline and evaluation metrics
• Serving architecture (online vs offline), freshness/latency
• Monitoring, bias/fairness, abuse/gaming, and iteration plan