• Architected a Federated Multi-Agent System leveraging Google Agent Development Kit (ADK) to unify three fragmented Lines of Business (Rx, Benefits, Provider) into a single intelligent interface.

• Designed a state-aware Orchestrator Agent that manages context across multi-turn conversations, dynamically routing intents to specialized sub-agents and solving the "siloed bot" problem.

• Deployed a Retrieval-Augmented Generation (RAG) pipeline to automate Claims Inquiries (12% of total volume), projected to deflect ~4 million calls annually and save $8M in operational costs.

• Led the first production deployment of CCAI Insights at scale, ingesting 35M+ annual calls into BigQuery for sentiment analysis, topic modeling, and advocate quality scoring.

• Engineered a "Digital Twin" Simulation Engine based on Factory Physics to model complex supply chain dynamics.

• Built a custom Directed Acyclic Graph (DAG) framework to propagate demand/supply constraints across network nodes, enabling stress-testing of inventory strategies before deployment.

• Developed Graph Traversal Algorithms for Bill of Materials (BOM) explosion, optimizing inventory planning and identifying critical bottlenecks in real-time.

• Generated massive-scale synthetic datasets via simulation to train "Supply Chain Copilot" models for strategic forecasting.

• Developed and deployed privacy-preserving fall detection models on constrained edge hardware (IntelNUC), utilizing exclusively IR/Depth data to maintain HIPAA compliance.

• Diagnosed critical thermal throttling failures in field devices by analyzing CPU telemetry; identified third-party hardware defects and optimized inference pipelines to run stably on 15W power budgets.

• Implemented Conv-LSTM architectures on temporal depth data to move from reactive fall detection to proactive intent prediction (predicting "exit-bed" motion 2 seconds in advance).

• Optimized deep learning models using pruning and quantization (PyTorch), achieving real-time FPS on compute-constrained edge devices.

• Conducted research on Self-Supervised Learning and Disentangled Representations for robotic pose estimation in unstructured environments.

• Designed Denoising Autoencoders to extract pose-pertinent features from synthetic data, enabling robust performance in high-noise real-world settings without manual labels.

• Benchmarked 3D-CNN vs. Conv-LSTM architectures for spatiotemporal modeling, establishing baselines for object manipulation tasks.