Showing 89 tagged AI roles in Telecom. Layout is a Fruchterman-Reingold force simulation, run server-side to convergence.
agent_orchestration ↔ llm_observability (43 JDs) llm_observability ↔ tool_use (14 JDs) llm_observability ↔ vector_db (12 JDs) llm_observability ↔ model_serving (25 JDs) inference_infra ↔ llm_observability (8 JDs) guardrails ↔ llm_observability (14 JDs) agent_orchestration ↔ tool_use (18 JDs) agent_orchestration ↔ vector_db (13 JDs) agent_orchestration ↔ model_serving (27 JDs) agent_orchestration ↔ inference_infra (10 JDs) agent_orchestration ↔ guardrails (14 JDs) tool_use ↔ vector_db (3 JDs) model_serving ↔ tool_use (6 JDs) inference_infra ↔ tool_use (3 JDs) guardrails ↔ tool_use (5 JDs) model_serving ↔ vector_db (9 JDs) inference_infra ↔ vector_db (3 JDs) guardrails ↔ vector_db (2 JDs) inference_infra ↔ model_serving (15 JDs) guardrails ↔ model_serving (8 JDs) guardrails ↔ inference_infra (2 JDs) agent_orchestration ↔ fine_tuning (20 JDs) agent_orchestration ↔ rag (38 JDs) agent_orchestration ↔ agent_research (10 JDs) fine_tuning ↔ tool_use (8 JDs) rag ↔ tool_use (12 JDs) agent_research ↔ tool_use (3 JDs) fine_tuning ↔ rag (19 JDs) agent_research ↔ fine_tuning (5 JDs) fine_tuning ↔ llm_observability (20 JDs) agent_research ↔ rag (9 JDs) llm_observability ↔ rag (37 JDs) agent_research ↔ llm_observability (9 JDs) agent_orchestration ↔ rlhf (5 JDs) rag ↔ rlhf (5 JDs) llm_observability ↔ rlhf (5 JDs) agent_orchestration ↔ multimodal (5 JDs) agent_orchestration ↔ evals (9 JDs) model_serving ↔ rag (21 JDs) multimodal ↔ rag (5 JDs) evals ↔ rag (5 JDs) fine_tuning ↔ model_serving (14 JDs) fine_tuning ↔ multimodal (4 JDs) evals ↔ fine_tuning (5 JDs) model_serving ↔ multimodal (5 JDs) evals ↔ model_serving (2 JDs) llm_observability ↔ multimodal (5 JDs) evals ↔ llm_observability (9 JDs) evals ↔ multimodal (2 JDs) rag ↔ vector_db (12 JDs) audio_speech ↔ rag (3 JDs) audio_speech ↔ vector_db (3 JDs) agent_orchestration ↔ audio_speech (4 JDs) audio_speech ↔ llm_observability (4 JDs) audio_speech ↔ model_serving (3 JDs) evals ↔ tool_use (4 JDs) fine_tuning ↔ guardrails (6 JDs) guardrails ↔ rag (9 JDs) evals ↔ guardrails (8 JDs) inference_infra ↔ rag (4 JDs) evals ↔ vector_db (1 JDs) agent_research ↔ vector_db (4 JDs) llm_observability ↔ recommender_systems (5 JDs) llm_observability ↔ search_ranking (5 JDs) interpretability ↔ llm_observability (3 JDs) llm_observability ↔ synthetic_data (3 JDs) llm_observability ↔ rl_post_training (3 JDs) llm_observability ↔ reward_modeling (3 JDs) llm_observability ↔ rl_robotics (3 JDs) embodied_ai ↔ llm_observability (3 JDs) agent_orchestration ↔ recommender_systems (4 JDs) agent_orchestration ↔ search_ranking (4 JDs) agent_orchestration ↔ interpretability (3 JDs) agent_orchestration ↔ synthetic_data (3 JDs) agent_orchestration ↔ rl_post_training (3 JDs) agent_orchestration ↔ reward_modeling (3 JDs) agent_orchestration ↔ rl_robotics (3 JDs) agent_orchestration ↔ embodied_ai (4 JDs) guardrails ↔ recommender_systems (1 JDs) guardrails ↔ search_ranking (1 JDs) guardrails ↔ interpretability (1 JDs) guardrails ↔ synthetic_data (1 JDs) agent_research ↔ guardrails (2 JDs) guardrails ↔ rl_post_training (1 JDs) guardrails ↔ rlhf (1 JDs) guardrails ↔ reward_modeling (1 JDs) guardrails ↔ rl_robotics (1 JDs) embodied_ai ↔ guardrails (1 JDs) rag ↔ recommender_systems (4 JDs) rag ↔ search_ranking (4 JDs) interpretability ↔ rag (3 JDs) rag ↔ synthetic_data (3 JDs) rag ↔ rl_post_training (3 JDs) rag ↔ reward_modeling (3 JDs) rag ↔ rl_robotics (3 JDs) embodied_ai ↔ rag (3 JDs) fine_tuning ↔ vector_db (7 JDs) recommender_systems ↔ vector_db (3 JDs) search_ranking ↔ vector_db (3 JDs) interpretability ↔ vector_db (3 JDs) synthetic_data ↔ vector_db (3 JDs) rl_post_training ↔ vector_db (3 JDs) rlhf ↔ vector_db (3 JDs) reward_modeling ↔ vector_db (3 JDs) rl_robotics ↔ vector_db (3 JDs) embodied_ai ↔ vector_db (3 JDs) fine_tuning ↔ recommender_systems (4 JDs) fine_tuning ↔ search_ranking (4 JDs) fine_tuning ↔ interpretability (3 JDs) fine_tuning ↔ synthetic_data (3 JDs) fine_tuning ↔ rl_post_training (3 JDs) fine_tuning ↔ rlhf (3 JDs) fine_tuning ↔ reward_modeling (3 JDs) fine_tuning ↔ rl_robotics (3 JDs) embodied_ai ↔ fine_tuning (3 JDs) model_serving ↔ recommender_systems (5 JDs) model_serving ↔ search_ranking (5 JDs) interpretability ↔ model_serving (3 JDs) model_serving ↔ synthetic_data (3 JDs) agent_research ↔ model_serving (5 JDs) model_serving ↔ rl_post_training (3 JDs) model_serving ↔ rlhf (3 JDs) model_serving ↔ reward_modeling (3 JDs) model_serving ↔ rl_robotics (3 JDs) embodied_ai ↔ model_serving (3 JDs) recommender_systems ↔ search_ranking (5 JDs) interpretability ↔ recommender_systems (3 JDs) recommender_systems ↔ synthetic_data (3 JDs) agent_research ↔ recommender_systems (3 JDs) recommender_systems ↔ rl_post_training (3 JDs) recommender_systems ↔ rlhf (3 JDs) recommender_systems ↔ reward_modeling (3 JDs) recommender_systems ↔ rl_robotics (3 JDs) embodied_ai ↔ recommender_systems (3 JDs) interpretability ↔ search_ranking (3 JDs) search_ranking ↔ synthetic_data (3 JDs) agent_research ↔ search_ranking (3 JDs) rl_post_training ↔ search_ranking (3 JDs) rlhf ↔ search_ranking (3 JDs) reward_modeling ↔ search_ranking (3 JDs) rl_robotics ↔ search_ranking (3 JDs) embodied_ai ↔ search_ranking (3 JDs) interpretability ↔ synthetic_data (3 JDs) agent_research ↔ interpretability (3 JDs) interpretability ↔ rl_post_training (3 JDs) interpretability ↔ rlhf (3 JDs) interpretability ↔ reward_modeling (3 JDs) interpretability ↔ rl_robotics (3 JDs) embodied_ai ↔ interpretability (3 JDs) agent_research ↔ synthetic_data (3 JDs) rl_post_training ↔ synthetic_data (3 JDs) rlhf ↔ synthetic_data (3 JDs) reward_modeling ↔ synthetic_data (3 JDs) rl_robotics ↔ synthetic_data (3 JDs) embodied_ai ↔ synthetic_data (3 JDs) agent_research ↔ rl_post_training (3 JDs) agent_research ↔ rlhf (3 JDs) agent_research ↔ reward_modeling (3 JDs) agent_research ↔ rl_robotics (3 JDs) agent_research ↔ embodied_ai (3 JDs) rl_post_training ↔ rlhf (3 JDs) reward_modeling ↔ rl_post_training (3 JDs) rl_post_training ↔ rl_robotics (3 JDs) embodied_ai ↔ rl_post_training (3 JDs) reward_modeling ↔ rlhf (3 JDs) rl_robotics ↔ rlhf (3 JDs) embodied_ai ↔ rlhf (3 JDs) reward_modeling ↔ rl_robotics (3 JDs) embodied_ai ↔ reward_modeling (3 JDs) embodied_ai ↔ rl_robotics (3 JDs) inference_infra ↔ recommender_systems (1 JDs) inference_infra ↔ search_ranking (1 JDs) multimodal ↔ tool_use (2 JDs) llm_observability ↔ vision (2 JDs) frontier_research ↔ llm_observability (2 JDs) code_gen ↔ llm_observability (2 JDs) agent_orchestration ↔ vision (2 JDs) agent_orchestration ↔ frontier_research (2 JDs) agent_orchestration ↔ code_gen (2 JDs) rag ↔ vision (2 JDs) frontier_research ↔ rag (2 JDs) code_gen ↔ rag (2 JDs) vector_db ↔ vision (2 JDs) multimodal ↔ vector_db (3 JDs) frontier_research ↔ vector_db (2 JDs) code_gen ↔ vector_db (2 JDs) fine_tuning ↔ vision (2 JDs) audio_speech ↔ fine_tuning (2 JDs) fine_tuning ↔ frontier_research (2 JDs) code_gen ↔ fine_tuning (2 JDs) model_serving ↔ vision (2 JDs) frontier_research ↔ model_serving (2 JDs) code_gen ↔ model_serving (2 JDs) recommender_systems ↔ vision (2 JDs) multimodal ↔ recommender_systems (2 JDs) audio_speech ↔ recommender_systems (2 JDs) frontier_research ↔ recommender_systems (2 JDs) code_gen ↔ recommender_systems (2 JDs) search_ranking ↔ vision (2 JDs) multimodal ↔ search_ranking (2 JDs) audio_speech ↔ search_ranking (2 JDs) frontier_research ↔ search_ranking (2 JDs) code_gen ↔ search_ranking (2 JDs) multimodal ↔ vision (2 JDs) audio_speech ↔ vision (2 JDs) frontier_research ↔ vision (2 JDs) interpretability ↔ vision (2 JDs) synthetic_data ↔ vision (2 JDs) agent_research ↔ vision (2 JDs) rl_post_training ↔ vision (2 JDs) rlhf ↔ vision (2 JDs) reward_modeling ↔ vision (2 JDs) rl_robotics ↔ vision (2 JDs) embodied_ai ↔ vision (2 JDs) code_gen ↔ vision (2 JDs) audio_speech ↔ multimodal (2 JDs) frontier_research ↔ multimodal (2 JDs) interpretability ↔ multimodal (2 JDs) multimodal ↔ synthetic_data (2 JDs) agent_research ↔ multimodal (2 JDs) multimodal ↔ rl_post_training (2 JDs) multimodal ↔ rlhf (2 JDs) multimodal ↔ reward_modeling (2 JDs) multimodal ↔ rl_robotics (2 JDs) embodied_ai ↔ multimodal (2 JDs) code_gen ↔ multimodal (2 JDs) audio_speech ↔ frontier_research (2 JDs) audio_speech ↔ interpretability (2 JDs) audio_speech ↔ synthetic_data (2 JDs) agent_research ↔ audio_speech (2 JDs) audio_speech ↔ rl_post_training (2 JDs) audio_speech ↔ rlhf (2 JDs) audio_speech ↔ reward_modeling (2 JDs) audio_speech ↔ rl_robotics (2 JDs) audio_speech ↔ embodied_ai (2 JDs) audio_speech ↔ code_gen (2 JDs) frontier_research ↔ interpretability (2 JDs) frontier_research ↔ synthetic_data (2 JDs) agent_research ↔ frontier_research (2 JDs) frontier_research ↔ rl_post_training (2 JDs) frontier_research ↔ rlhf (2 JDs) frontier_research ↔ reward_modeling (2 JDs) frontier_research ↔ rl_robotics (2 JDs) embodied_ai ↔ frontier_research (2 JDs) code_gen ↔ frontier_research (2 JDs) code_gen ↔ interpretability (2 JDs) code_gen ↔ synthetic_data (2 JDs) agent_research ↔ code_gen (2 JDs) code_gen ↔ rl_post_training (2 JDs) code_gen ↔ rlhf (2 JDs) code_gen ↔ reward_modeling (2 JDs) code_gen ↔ rl_robotics (2 JDs) code_gen ↔ embodied_ai (2 JDs) recommender_systems ↔ tool_use (1 JDs) search_ranking ↔ tool_use (1 JDs) tool_use ↔ vision (1 JDs) audio_speech ↔ tool_use (1 JDs) frontier_research ↔ tool_use (1 JDs) interpretability ↔ tool_use (1 JDs) synthetic_data ↔ tool_use (1 JDs) rl_post_training ↔ tool_use (1 JDs) rlhf ↔ tool_use (1 JDs) reward_modeling ↔ tool_use (1 JDs) rl_robotics ↔ tool_use (1 JDs) embodied_ai ↔ tool_use (1 JDs) code_gen ↔ tool_use (1 JDs) agent_orchestration ↔ multi_agent (1 JDs) multi_agent ↔ tool_use (1 JDs) fine_tuning ↔ multi_agent (1 JDs) multi_agent ↔ rag (1 JDs) multi_agent ↔ vector_db (1 JDs) llm_observability ↔ multi_agent (1 JDs) evals ↔ inference_infra (1 JDs) fine_tuning ↔ inference_infra (4 JDs) inference_infra ↔ multimodal (2 JDs) agent_research ↔ inference_infra (1 JDs) embodied_ai ↔ inference_infra (1 JDs) LLM observability
N=50 JDs
Top co-occur: Agent orchestration ×43 · RAG ×37 · Model serving ×25 LLM observability Agent orchestration
N=60 JDs
Top co-occur: LLM observability ×43 · RAG ×38 · Model serving ×27 Agent orchestration Tool use
N=18 JDs
Top co-occur: Agent orchestration ×18 · LLM observability ×14 · RAG ×12 Tool use Vector DB
N=13 JDs
Top co-occur: Agent orchestration ×13 · LLM observability ×12 · RAG ×12 Model serving
N=41 JDs
Top co-occur: Agent orchestration ×27 · LLM observability ×25 · RAG ×21 Model serving Inference infra
N=16 JDs
Top co-occur: Model serving ×15 · Agent orchestration ×10 · LLM observability ×8 Inference infra Guardrails
N=16 JDs
Top co-occur: LLM observability ×14 · Agent orchestration ×14 · RAG ×9 Guardrails Fine-tuning
N=22 JDs
Top co-occur: Agent orchestration ×20 · LLM observability ×20 · RAG ×19 Fine-tuning RAG
N=40 JDs
Top co-occur: Agent orchestration ×38 · LLM observability ×37 · Model serving ×21 RAG Agent research
N=10 JDs
Top co-occur: Agent orchestration ×10 · RAG ×9 · LLM observability ×9 RLHF
N=5 JDs
Top co-occur: Agent orchestration ×5 · RAG ×5 · LLM observability ×5 Multimodal
N=6 JDs
Top co-occur: Agent orchestration ×5 · RAG ×5 · Model serving ×5 Evals
N=11 JDs
Top co-occur: Agent orchestration ×9 · LLM observability ×9 · Guardrails ×8 Audio & speech
N=4 JDs
Top co-occur: Agent orchestration ×4 · LLM observability ×4 · RAG ×3 Recommender systems
N=7 JDs
Top co-occur: LLM observability ×5 · Model serving ×5 · Search & ranking ×5 Vision
N=5 JDs
Top co-occur: LLM observability ×2 · Agent orchestration ×2 · RAG ×2 Search & ranking
N=5 JDs
Top co-occur: LLM observability ×5 · Model serving ×5 · Recommender systems ×5 Interpretability
N=3 JDs
Top co-occur: LLM observability ×3 · Agent orchestration ×3 · RAG ×3 Synthetic data
N=3 JDs
Top co-occur: LLM observability ×3 · Agent orchestration ×3 · RAG ×3 RL post-training
N=3 JDs
Top co-occur: LLM observability ×3 · Agent orchestration ×3 · RAG ×3 Reward modeling
N=3 JDs
Top co-occur: LLM observability ×3 · Agent orchestration ×3 · RAG ×3 RL robotics
N=3 JDs
Top co-occur: LLM observability ×3 · Agent orchestration ×3 · RAG ×3 Embodied AI
N=4 JDs
Top co-occur: Agent orchestration ×4 · LLM observability ×3 · RAG ×3 Frontier research
N=3 JDs
Top co-occur: LLM observability ×2 · Agent orchestration ×2 · RAG ×2 Code gen
N=2 JDs
Top co-occur: LLM observability ×2 · Agent orchestration ×2 · RAG ×2 Data pipeline
N=1 JDs Multi-agent
N=1 JDs
Top co-occur: Agent orchestration ×1 · Tool use ×1 · Fine-tuning ×1 Forecasting
N=2 JDs 28 tags · 277 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.