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

inference_infra ↔ model_serving (25 JDs)model_serving ↔ training_infra (2 JDs)inference_infra ↔ training_infra (2 JDs)agent_orchestration ↔ llm_observability (83 JDs)agent_orchestration ↔ tool_use (64 JDs)llm_observability ↔ tool_use (37 JDs)evals ↔ model_serving (21 JDs)evals ↔ llm_observability (42 JDs)evals ↔ fine_tuning (19 JDs)llm_observability ↔ model_serving (45 JDs)fine_tuning ↔ model_serving (19 JDs)fine_tuning ↔ llm_observability (26 JDs)agent_orchestration ↔ evals (41 JDs)agent_orchestration ↔ rag (49 JDs)evals ↔ tool_use (21 JDs)rag ↔ tool_use (20 JDs)evals ↔ rag (27 JDs)llm_observability ↔ rag (49 JDs)agent_orchestration ↔ guardrails (33 JDs)agent_orchestration ↔ model_serving (37 JDs)guardrails ↔ tool_use (8 JDs)model_serving ↔ tool_use (14 JDs)guardrails ↔ llm_observability (37 JDs)guardrails ↔ rag (16 JDs)guardrails ↔ model_serving (13 JDs)model_serving ↔ rag (26 JDs)agent_orchestration ↔ vector_db (14 JDs)agent_orchestration ↔ inference_infra (11 JDs)rag ↔ vector_db (19 JDs)inference_infra ↔ rag (7 JDs)inference_infra ↔ vector_db (4 JDs)model_serving ↔ vector_db (15 JDs)fine_tuning ↔ rag (24 JDs)agent_orchestration ↔ fine_tuning (21 JDs)agent_orchestration ↔ recommender_systems (9 JDs)agent_orchestration ↔ search_ranking (7 JDs)llm_observability ↔ vector_db (17 JDs)llm_observability ↔ recommender_systems (9 JDs)llm_observability ↔ search_ranking (6 JDs)rag ↔ recommender_systems (7 JDs)rag ↔ search_ranking (6 JDs)fine_tuning ↔ vector_db (12 JDs)recommender_systems ↔ vector_db (7 JDs)search_ranking ↔ vector_db (6 JDs)fine_tuning ↔ recommender_systems (8 JDs)fine_tuning ↔ search_ranking (6 JDs)model_serving ↔ recommender_systems (11 JDs)model_serving ↔ search_ranking (7 JDs)recommender_systems ↔ search_ranking (9 JDs)evals ↔ guardrails (22 JDs)guardrails ↔ inference_infra (5 JDs)inference_infra ↔ tool_use (2 JDs)evals ↔ vector_db (9 JDs)evals ↔ inference_infra (5 JDs)inference_infra ↔ llm_observability (10 JDs)agent_orchestration ↔ agent_research (9 JDs)fine_tuning ↔ tool_use (10 JDs)tool_use ↔ vector_db (6 JDs)guardrails ↔ vector_db (2 JDs)audio_speech ↔ evals (2 JDs)audio_speech ↔ inference_infra (2 JDs)audio_speech ↔ model_serving (3 JDs)fine_tuning ↔ guardrails (4 JDs)agent_orchestration ↔ code_gen (2 JDs)code_gen ↔ tool_use (2 JDs)evals ↔ synthetic_data (3 JDs)agent_orchestration ↔ synthetic_data (3 JDs)llm_observability ↔ synthetic_data (3 JDs)model_serving ↔ synthetic_data (2 JDs)guardrails ↔ synthetic_data (2 JDs)recommender_systems ↔ tool_use (4 JDs)search_ranking ↔ tool_use (4 JDs)evals ↔ recommender_systems (5 JDs)evals ↔ search_ranking (5 JDs)agent_research ↔ tool_use (2 JDs)agent_research ↔ evals (3 JDs)agent_research ↔ fine_tuning (4 JDs)agent_research ↔ model_serving (4 JDs)audio_speech ↔ llm_observability (2 JDs)agent_research ↔ llm_observability (4 JDs)agent_research ↔ rag (3 JDs)agent_research ↔ vector_db (2 JDs)Model serving N=72 JDs Top co-occur: LLM observability ×45 · Agent orchestration ×37 · RAG ×26Model servingInference infra N=25 JDs Top co-occur: Model serving ×25 · Agent orchestration ×11 · LLM observability ×10Training infra N=2 JDs Top co-occur: Model serving ×2 · Inference infra ×2Agent orchestration N=157 JDs Top co-occur: LLM observability ×83 · Tool use ×64 · RAG ×49Agent orchestrationLLM observability N=115 JDs Top co-occur: Agent orchestration ×83 · RAG ×49 · Model serving ×45LLM observabilityTool use N=64 JDs Top co-occur: Agent orchestration ×64 · LLM observability ×37 · Evals ×21Tool useEvals N=54 JDs Top co-occur: LLM observability ×42 · Agent orchestration ×41 · RAG ×27EvalsFine-tuning N=31 JDs Top co-occur: LLM observability ×26 · RAG ×24 · Agent orchestration ×21RAG N=60 JDs Top co-occur: Agent orchestration ×49 · LLM observability ×49 · Evals ×27RAGGuardrails N=46 JDs Top co-occur: LLM observability ×37 · Agent orchestration ×33 · Evals ×22GuardrailsVector DB N=19 JDs Top co-occur: RAG ×19 · LLM observability ×17 · Model serving ×15Recommender systems N=19 JDs Top co-occur: Model serving ×11 · Agent orchestration ×9 · LLM observability ×9Search & ranking N=9 JDs Top co-occur: Recommender systems ×9 · Agent orchestration ×7 · Model serving ×7Agent research N=10 JDs Top co-occur: Agent orchestration ×9 · Fine-tuning ×4 · Model serving ×4Audio & speech N=3 JDs Top co-occur: Model serving ×3 · Evals ×2 · Inference infra ×2Code gen N=2 JDs Top co-occur: Agent orchestration ×2 · Tool use ×2Synthetic data N=3 JDs Top co-occur: Evals ×3 · Agent orchestration ×3 · LLM observability ×3Data pipeline N=1 JDsInterpretability N=1 JDs
19 tags · 82 co-occurrence edges · min edge weight 2. 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.

Tag12-week trendLast 4wPrior 4wΔTotal
tool_use
6 roles
Feb 23
0 roles
Mar 2
3 roles
9
2 roles
16
5 roles
23
3 roles
30
2 roles
Apr 6
5 roles
13
7 roles
20
8 roles
27
12 roles
May 4
0 roles
11
3212↑+2064
agent_orchestration
17 roles
Feb 23
5 roles
Mar 2
6 roles
9
9 roles
16
9 roles
23
7 roles
30
10 roles
Apr 6
8 roles
13
18 roles
20
12 roles
27
14 roles
May 4
0 roles
11
5235↑+17157
llm_observability
11 roles
Feb 23
4 roles
Mar 2
8 roles
9
9 roles
16
3 roles
23
4 roles
30
7 roles
Apr 6
5 roles
13
12 roles
20
9 roles
27
14 roles
May 4
0 roles
11
4023↑+17115
model_serving
9 roles
Feb 23
1 role
Mar 2
4 roles
9
3 roles
16
2 roles
23
2 roles
30
4 roles
Apr 6
2 roles
13
14 roles
20
1 role
27
8 roles
May 4
0 roles
11
2511↑+1472
evals
9 roles
Feb 23
0 roles
Mar 2
5 roles
9
2 roles
16
2 roles
23
2 roles
30
2 roles
Apr 6
5 roles
13
6 roles
20
5 roles
27
5 roles
May 4
0 roles
11
218↑+1354
guardrails
3 roles
Feb 23
5 roles
Mar 2
2 roles
9
3 roles
16
0 roles
23
3 roles
30
6 roles
Apr 6
4 roles
13
8 roles
20
5 roles
27
3 roles
May 4
0 roles
11
2012↑+846
rag
7 roles
Feb 23
1 role
Mar 2
2 roles
9
3 roles
16
1 role
23
2 roles
30
5 roles
Apr 6
3 roles
13
3 roles
20
8 roles
27
3 roles
May 4
0 roles
11
1711↑+660
recommender_systems
5 roles
Feb 23
0 roles
Mar 2
1 role
9
1 role
16
2 roles
23
0 roles
30
0 roles
Apr 6
2 roles
13
3 roles
20
1 role
27
0 roles
May 4
0 roles
11
63↑+319
inference_infra
0 roles
Feb 23
0 roles
Mar 2
2 roles
9
1 role
16
1 role
23
1 role
30
3 roles
Apr 6
1 role
13
6 roles
20
0 roles
27
1 role
May 4
0 roles
11
86↑+225
vector_db
5 roles
Feb 23
0 roles
Mar 2
1 role
9
1 role
16
0 roles
23
0 roles
30
0 roles
Apr 6
0 roles
13
1 role
20
1 role
27
0 roles
May 4
0 roles
11
21↑+119
fine_tuning
7 roles
Feb 23
0 roles
Mar 2
3 roles
9
2 roles
16
1 role
23
1 role
30
0 roles
Apr 6
1 role
13
2 roles
20
0 roles
27
1 role
May 4
0 roles
11
44·031
agent_research
1 role
Feb 23
0 roles
Mar 2
0 roles
9
0 roles
16
0 roles
23
1 role
30
1 role
Apr 6
0 roles
13
0 roles
20
0 roles
27
1 role
May 4
0 roles
11
12↓-110