Ensemble AI
9-agent ensemble system with LSTM, CNN, and RNN specialists. 25-year temporal validation with statistical significance testing, Sharpe ratio analysis, and ablation studies.
9-agent ensemble system with parallel model-specialist coordination across 25 years of temporal validation data. LSTM, CNN, and RNN operate as independent specialists. Dynamic weighting under non-stationary conditions.
Single-model approaches to temporal prediction suffer from inherent architectural bias. No single architecture optimally handles multi-dimensional temporal data.
Parallel specialist coordination with adaptive weighting. Three model architectures operate as independent specialist agents. Ensemble coordinator adjusts weights based on rolling accuracy windows. Validation rigor through significance testing and ablation analysis.
Applied AI Engineer
Implement and tune LSTM/CNN/RNN specialists, ensemble coordination, and PyTorch training workflows.
Apply →ML Engineer
Build temporal validation pipeline, backtesting framework, and rolling evaluation for non-stationary regimes.
Apply →Backend Engineer
Data pipelines, job orchestration, and integration with AWS and numpy/pandas workloads at scale.
Apply →Data Engineer
25-year validation datasets, preprocessing, and feature engineering for temporal ensemble models.
Apply →AI Researcher
Significance testing, ablation studies, Sharpe and ensemble methodology; support publication-quality analysis.
Apply →DevOps / MLOps
Cloud jobs for long-running training, experiment tracking, and monitoring for distributed ensemble workloads.
Apply →