Architected and developed a scalable agentic AI platform designed for production environments. Includes declarative agent specifications with YAML-based configuration, multi-tier memory systems (working, episodic, semantic), integration with vector stores and relational databases, comprehensive evaluation pipelines for agent testing.

Graduate project supervised by Prof. S. Toor & Prof. P. Singh. Conducted systematic empirical study of different type of MoE architectures on CIFAR-10.

Built a custom Transformer architecture from scratch in PyTorch to deepen understanding of attention mechanisms and sequence modeling. Implemented multi-head attention, positional encoding, and encoder-decoder architecture with detailed documentation of design choices.

Developed a distributed text summarization system for large-scale document processing. Implemented parallel processing architecture with task distribution, efficient batching, and result aggregation for high-throughput summarization workloads.

User-friendly apartment search with natural language queries. Built preference parsing system, intelligent ranking algorithms, and application guidance features to simplify the housing search experience.