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 14 tagged AI roles in Aerospace. Layout is a Fruchterman-Reingold force simulation, run server-side to convergence.

fine_tuning ↔ rag (4 JDs)rag ↔ semantic_search (1 JDs)llm_observability ↔ rag (1 JDs)fine_tuning ↔ semantic_search (1 JDs)fine_tuning ↔ llm_observability (1 JDs)llm_observability ↔ semantic_search (1 JDs)fine_tuning ↔ model_serving (3 JDs)evals ↔ fine_tuning (1 JDs)evals ↔ model_serving (1 JDs)multimodal ↔ vision (1 JDs)fine_tuning ↔ multimodal (2 JDs)model_serving ↔ multimodal (1 JDs)multimodal ↔ rag (2 JDs)fine_tuning ↔ vision (1 JDs)model_serving ↔ vision (1 JDs)rag ↔ vision (1 JDs)model_serving ↔ rag (4 JDs)agent_orchestration ↔ rag (3 JDs)agent_orchestration ↔ model_serving (2 JDs)rag ↔ vector_db (2 JDs)agent_orchestration ↔ fine_tuning (2 JDs)agent_orchestration ↔ multimodal (1 JDs)inference_infra ↔ model_serving (2 JDs)model_serving ↔ vector_db (1 JDs)inference_infra ↔ rag (1 JDs)agent_orchestration ↔ inference_infra (1 JDs)fine_tuning ↔ inference_infra (1 JDs)inference_infra ↔ vector_db (1 JDs)agent_orchestration ↔ vector_db (1 JDs)fine_tuning ↔ vector_db (1 JDs)agent_orchestration ↔ embodied_ai (1 JDs)RAG N=7 JDs Top co-occur: Fine-tuning ×4 · Model serving ×4 · Agent orchestration ×3RAGFine-tuning N=5 JDs Top co-occur: RAG ×4 · Model serving ×3 · Multimodal ×2Fine-tuningSemantic search N=1 JDs Top co-occur: RAG ×1 · Fine-tuning ×1 · LLM observability ×1LLM observability N=1 JDs Top co-occur: RAG ×1 · Fine-tuning ×1 · Semantic search ×1Model serving N=9 JDs Top co-occur: RAG ×4 · Fine-tuning ×3 · Agent orchestration ×2Model servingEvals N=1 JDs Top co-occur: Fine-tuning ×1 · Model serving ×1Multimodal N=3 JDs Top co-occur: Fine-tuning ×2 · RAG ×2 · Vision ×1MultimodalVision N=1 JDs Top co-occur: Multimodal ×1 · Fine-tuning ×1 · Model serving ×1Agent orchestration N=4 JDs Top co-occur: RAG ×3 · Model serving ×2 · Fine-tuning ×2Agent orchestrationVector DB N=2 JDs Top co-occur: RAG ×2 · Model serving ×1 · Inference infra ×1Inference infra N=2 JDs Top co-occur: Model serving ×2 · RAG ×1 · Agent orchestration ×1Embodied AI N=1 JDs Top co-occur: Agent orchestration ×1
12 tags · 31 co-occurrence edges · min edge weight 1. Bubble area ∝ JDs containing tag · edge thickness ∝ co-occurrence count. Hover any node for top-3 partners; click to see the JDs.

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.

No velocity data yet.