Edwin

Applied Machine Learning for High-Throughput Systems • Real-time streaming ML with Spark Structured Streaming or Flink • Predictive analytics on IoT sensor data (e.g., drones, radars) • Synthetic data generation to accelerate training cycles • Model retraining pipelines using event triggers (e.g., in Airflow + MLflow) Agentic & Autonomous AI Systems • Multi-agent coordination for economic forecasting • Recommender agents for social/behavioral modeling • Agentic systems for policy simulation or campaign strategy • Autonomous pipelines for self-healing ML workflows AI for Government, Defense & Infrastructure • Mission-driven AI for surveillance or threat detection • AI for public-sector optimization (e.g., resource allocation, digital transformation) • AI + policy forecasting (e.g., legislative bill impact models) • Agent-based modeling for workforce/economic scenarios AI in Capital Markets & Fintech • Reinforcement learning for trading strategies • Market anomaly detection using transformer models • Agentic AI for macroeconomic scenario modeling • Natural language processing (NLP) for regulatory filings, sentiment Multilingual & Cultural AI Systems • NLP models for multilingual threat intelligence • AI models tuned for cross-cultural legislative or diplomatic data • Machine translation evaluation for real-time communication risk MLOps & Scalable AI Infrastructure • Model lifecycle governance with MLflow + Databricks • CI/CD for ML pipelines using dbt, Airflow, and cloud-native tooling • Federated learning systems across secure domains • Observability frameworks (e.g., Grafana + Prometheus for ML metrics) AI for Workforce Optimization • Attrition prediction and retention recommender systems • Skills-matching engines powered by LLM embeddings • AI assistants for hiring, upskilling, and strategic org design • Simulation models for labor policy changes