Reference architectures, technical tools, and published work across cloud data platforms, enterprise migrations, and AI infrastructure. Each project explains the problem, my contribution, and the decisions behind the solution.

Co-authored a real-time personalization guide connecting Databricks stream processing with Redis serving. The reference demo sustained 100,000 events per second at 157 ms p99 end-to-end latency.

Authored Microsoft Learn architecture guidance for low-latency reads with SQL as the source of truth, including partial failures, cache repair, and operational tradeoffs.

Built a toolkit to discover Redis resources across Azure subscriptions and collect inventory and usage metrics for sizing and migration planning.

Authored a Microsoft Learn reference architecture for keeping Azure Managed Redis caches current as Azure Cosmos DB data changes, covering data freshness, performance, and operational tradeoffs.

Authored the Azure Managed Redis Well-Architected service guide, with recommendations for reliability, security, cost optimization, operational excellence, and performance efficiency.

Presented a Microsoft Build 2026 lightning talk on Azure Managed Redis for Foundry agents and served as a Redis Expert on Azure Redis booth duty.

Co-authored a blog post showing how Azure Managed Redis and Azure Databricks can work together to support real-time feature serving for latency-sensitive AI and machine learning applications.

Presented a Microsoft Ignite 2025 breakout session demonstrating a semantic caching demo I built, supported attendees as a Microsoft product expert on booth duty, and passed the DP-900 certification onsite.

Presented at Fractal Tech in NYC on a Redis-built pattern: context-enabled semantic caching, semantic cache hits adapted with user context for faster, cheaper, more personalized LLM outputs.

Contributed to Redis’ official “agent skills” collection: packaged guidance and resources that help AI coding agents make better Redis decisions (data structures, vector search, caching, performance).

Co-authored a practical guide showing how cache-aside with Azure Managed Redis reduces Cosmos DB RU costs and improves read latency, without rewriting the application.

Featured at Microsoft Ignite 2025. Created an end-to-end semantic caching demo with real-time streaming plus an interactive cost & sizing calculator to quantify Redis ROI vs. raw LLM spend.

Built a hands-on Python notebook that demonstrates CESC: semantic cache matching with Redis vector search, then personalization using user context to transform generic cached responses into user-relevant answers.

Wrote and published a deep-dive on “Context-Enabled Semantic Caching” (CESC): combining semantic cache hits with lightweight personalization and optional RAG context to cut latency/cost while improving relevance for enterprise AI apps.

Spearheaded the technological modernization of my alma mater's Sigma Nu chapter, implementing a new digital infrastructure to support operations and a successful $40,000 fundraising campaign.

A comprehensive, interactive machine learning application designed to predict customer churn for a telecommunications company, guiding users through each step of the ML workflow.

This project details the creation of my personal website – a next-generation digital presence built using Next.js & React, hosted on AWS.