Software Engineer focused on building AI-driven backend systems and real-world applications.
- Designing systems that combine data + decision-making + user experience
- Experienced in LLM integration, APIs, and scalable architectures
- Strong focus on turning ambiguity into working systems
"I build systems that work under real-world constraints."
- 🚀 Backend APIs & System Design
- 🤖 AI/ML + LLM Integration
- 📊 Data-Driven Systems
- ☁️ Cloud & Scalable Architectures
- 🔄 Full-Stack Development
What it does:
Helps users decide whether to use points or cash for flights
Key Contributions:
- Built backend integrating flight pricing + award availability APIs
- Designed decision engine using CPP (cents-per-point logic)
- Integrated LLM explanations for user-facing insights
- Aligned frontend, backend, and conversational AI flows
Engineering Challenges:
- Handling inconsistent external APIs
- Translating vague requirements into system logic
- Maintaining real-time performance across services
- Built emotion-aware system using transformer-based models
- Generated personalized affirmations using LLMs
- Designed engaging UI with character-based interaction
- Built Android app using Firebase
- Designed structured medical data storage
- Focused on secure and scalable architecture
University of Pittsburgh — Graduate Research
- Applied ML for social science & policy insights
- Conducted statistical analysis (chi-square, behavioral patterns)
- Built structured datasets for research
- Built ML pipeline with XGBoost (77% accuracy)
- Integrated SHAP for interpretability
- Designed system for legal data complexity
- Achieved 92% accuracy
- Used ResNet50 transfer learning
- Increased dataset with AI-generated images
- Built ML model predicting color trends
- Applied statistical and data-driven insights
- Advanced LLM + system integration
- AI-driven decision systems
- Backend optimization for real-time applications