Projects
Selected projects demonstrating real-world AI system design and deployment. Each case study covers the problem, architecture decisions, and production lessons learned.
Real-Time Fraud Detection System
Legacy rule-based system missing 40% of fraud cases while generating excessive false positives
Enterprise RAG Pipeline
Engineers spending 30% of time searching for answers across fragmented documentation
ML Platform on Kubernetes
Data scientists waiting 2+ weeks for infrastructure provisioning to run experiments
Personalized Recommendation Engine
Static recommendations leading to declining engagement and conversion rates
Automated Visual Quality Inspection
Manual inspection missing subtle defects and creating production bottlenecks
Note on Project Details
Some project details have been generalized to protect proprietary information. Architecture diagrams and metrics represent the general approach rather than exact production specifications. Happy to discuss specific technical decisions in more depth during conversations.