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.
Rashanjot Kaur - AI Agent Architect
Designed 9-agent ensemble with LSTM/CNN/RNN specialist coordination.
7 open role(s)
Applied AI Engineer - Open
Implement and tune LSTM/CNN/RNN specialists, ensemble coordination, and PyTorch training workflows.
Apply →ML Engineer - Open
Build temporal validation pipeline, backtesting framework, and rolling evaluation for non-stationary regimes.
Apply →Backend Engineer - Open
Data pipelines, job orchestration, and integration with AWS Bedrock and numpy/pandas workloads at scale.
Apply →Frontend Engineer - Open
Streamlit dashboards for backtests, Sharpe views, and ensemble diagnostics.
Apply →Data Engineer - Open
25-year validation datasets, preprocessing, and feature engineering for temporal ensemble models.
Apply →AI Researcher - Open
Significance testing, ablation studies, Sharpe and ensemble methodology; support publication-quality analysis.
Apply →DevOps / MLOps - Open
Cloud jobs for long-running training, experiment tracking, and monitoring for distributed ensemble workloads.
Apply →