Search-Augmented AI Agent [Python] [Streamlit] [Claude] [RAG Architecture]

This project moves beyond standard chatbot interfaces to demonstrate proficiency in designing and deploying custom Retrieval-Augmented Generation (RAG) applications. It focuses on integrating live web scanning capabilities with advanced LLMs to synthesize targeted business intelligence.

• Architecture: Designed a custom web-based application utilizing Streamlit and Claude to dynamically ingest and analyze complex corporate landscapes.

• Orchestration: Implemented a RAG framework to scan the live web, grounding the agent's reasoning in up-to-date, real-world data rather than static training weights.

• Application: Built specifically to automate deep-dive company research, extracting actionable insights for strategic decision-making and interview preparation.

Ready to discuss specific advantages regarding contextual data retrieval and dynamic prompt engineering.

Grid Stress Simulator [Python] [Snowflake] [Simulation] [Utility Analytics]

I bridge the gap between theoretical data science and physical infrastructure reliability. Beyond basic forecasting, I architected a custom simulation engine designed to model utility grid resilience under varying stress conditions.

• Architecture: Engineered a robust data pipeline integrating Python with a Snowflake backend to manage and process complex, large-scale utility simulations.

• Modeling: Designed mathematical frameworks to simulate stress scenarios on physical infrastructure, prioritizing point-in-time correctness and reliable performance metrics.

• Application: Developed as a standalone analytics tool to evaluate grid vulnerabilities and provide actionable foresight for infrastructure resilience planning.

I combine the statistical rigor of a traditional Data Scientist with modern cloud-native engineering to build simulations that reflect real-world physical constraints.

Predictive Maintenance Engine (NASA CMAPSS) [Snowflake] [MLflow] [Time-Series] [Predictive Modeling]

This project demonstrates end-to-end machine learning lifecycle management on high-frequency sensor data. It focuses on the mathematical rigor required to predict mechanical failure before it occurs using complex time-series telemetry.

• Architecture: Engineered a seamless data ingestion and modeling pipeline leveraging Snowflake and MLflow to manage the complete lifecycle of NASA CMAPSS engine telemetry.

• Optimization: Applied rigorous feature engineering and model evaluation protocols to accurately capture mechanical degradation signals over time without temporal leakage.

• Application: Built to prove the viability of deploying proactive maintenance models that shift operational strategies from reactive repairs to preventive intervention.

Ready to discuss specific approaches to time-series model validation and predictive drift monitoring.