Tech-tag co-occurrence
Every AI role gets tagged from a 35-term controlled vocabulary spanning agent / eval / training / inference / modality clusters. Tags that frequently appear together in the same JD pull each other close; thicker edges = more co-occurrences. The clusters that emerge organically are the real sub-disciplines of applied AI engineering right now.
All sectors · 4742Enterprise · 1275AI Frontier · 868Industrial · 650Data AI · 526Consumer · 337Banking · 239Fintech · 220Robotics · 127Defense · 115Pharma · 101Media · 73Retail · 62Hospitality · 62Telecom · 27Seattle · 27Aerospace · 14Insurance · 12Healthtech · 7
Showing 868 tagged AI roles in AI Frontier. Layout is a Fruchterman-Reingold force simulation, run server-side to convergence.
Tag velocity · last 4 weeks vs prior 4
Which technologies are hot, which are cooling. Sparkline = 12 weeks of unique roles tagged with each term, last bar on the right is this week. Sorted by absolute pickup. Tags with under 10 lifetime mentions are hidden as noise.
| Tag | 12-week trend | Last 4w | Prior 4w | Δ |
|---|---|---|---|---|
| agent_orchestration | 98 | 48 | ↑+50 | |
| model_serving | 105 | 57 | ↑+48 | |
| llm_observability | 69 | 42 | ↑+27 | |
| rag | 50 | 23 | ↑+27 | |
| evals | 66 | 41 | ↑+25 | |
| inference_infra | 45 | 23 | ↑+22 | |
| tool_use | 31 | 10 | ↑+21 | |
| agent_research | 15 | 6 | ↑+9 | |
| multimodal | 18 | 9 | ↑+9 | |
| code_gen | 14 | 5 | ↑+9 | |
| vector_db | 11 | 4 | ↑+7 | |
| frontier_research | 12 | 7 | ↑+5 | |
| vision | 3 | 2 | ↑+1 | |
| rl_post_training | 22 | 22 | ·0 | |
| pretraining | 7 | 7 | ·0 | |
| interpretability | 2 | 2 | ·0 | |
| search_ranking | 4 | 4 | ·0 | |
| guardrails | 22 | 23 | ↓-1 | |
| fine_tuning | 48 | 50 | ↓-2 | |
| recommender_systems | 2 | 4 | ↓-2 | |
| synthetic_data | 10 | 30 | ↓-20 | |
| audio_speech | 16 | 40 | ↓-24 |