"I build for the p99. My focus is on architecting high-availability backends, optimizing distributed data layers, and bridging the gap between Generative AI and production-grade infrastructure."

• ⚡ Latency Optimization: Reduced API response times by 35% at ReplyQuickAI through async orchestration and server-side caching.
• 🏗️ Infrastructure Scale: Architected backends supporting 100K+ concurrent requests for real-time communication workflows.
• 💾 Database Engineering: Increased data retrieval throughput by 40% via PostgreSQL query tuning and Prisma ORM optimization.
• 🎓 Academic Excellence: Maintaining a 3.8/4.0 GPA in MS Computer Science.

• The Challenge: Real-time multimodal analysis for social media metadata generation.
• The Solution: Built a low-latency pipeline using Google Gemini 2.0 Flash and Python, focusing on prompt reliability and token efficiency.
• Stack: React Python Gemini API FastAPI

• The Challenge: Mapping high-dimensional facial expression data to sub-second API triggers.
• The Solution: Implemented a CNN-based classifier with TensorFlow/OpenCV, optimized for low-latency data streaming and Spotify API integration.
• Stack: TensorFlow OpenCV Flask Spotify API

• 🔭 Investigating: Vector Database scaling for Enterprise RAG (Retrieval-Augmented Generation).
• 💬 Ask me about: Why I prioritize Relational Integrity over NoSQL for mission-critical backends.
• ⚡ Goal: Engineering the next generation of global-scale AI infrastructure.

📍 United States • Open to Relocation • Looking for 2026 Roles