Software Engineer | AI Systems & Full-Stack Developer | Builder | Santa Clara, California | Ex-Capgemini | MS CS @ Santa Clara University
I build end-to-end intelligent systems — from scalable backend APIs to AI-powered applications used by real users.
- Applied AI (RAG, LLMs, multimodal systems)
- Backend & Distributed Systems
- Full-stack product development (web + mobile)
I care about shipping real products, not just prototypes.
- AI-powered systems (RAG, multimodal pipelines)
- Scalable backend systems (Flask + PostgreSQL)
- roduction-ready full-stack applications
Salus Wellness — Full-Stack Engineer
- Contributed to a production-grade wellness platform with web, mobile, and backend systems
- Built backend services for auth, scheduling, payments (Stripe), and messaging using Flask + Supabase
- Integrated AI-powered insights (GPT) for client recommendations and coaching workflows
- Worked on monorepo architecture (Turborepo) with shared types, APIs, and scalable infra
Sentari AI — Software Development Engineer (Full-Stack)
- Built & secured backend with 10+ Flask APIs (OAuth + JWT) for journaling, subscriptions & emotion tracking
- Integrated Whisper + GPT pipelines → improved transcription accuracy by 40%
- Shipped React Native iOS app serving 1K+ users
HCI Lab (SCU) — Graduate Research Assistant
- Built FocusMode Chrome extension used by 3,600+ users
- Designed AWS Lambda + DynamoDB backend handling 10K+ requests/day @ 99.9% uptime
- Improved dev velocity by 20% via CI/CD + monitoring
Capgemini — Software Engineer
- Worked on Android OS services for Logitech video conferencing devices
- Fixed race conditions & critical bugs, improving system stability
- Optimized OTA updates via S3 → 20% faster installs
- Built multi-tenant RAG system (Flask + pgvector + Gemini)
- Supports semantic search + citation-based QA over PDFs
- Designed scalable ingestion → embedding → retrieval pipeline
- Full-stack system (FastAPI + React) for emotional manipulation detection
- Combines rule-based + AI reasoning for risk classification
- Privacy-first (no data storage)
- Built multimodal pipeline using GPT-4 Vision + DALL·E 3
- Converts multiple images → structured prompt → generated composite
- Saves ~70% manual effort
- Streamlit + LangChain + ChromaDB + Gemini system
- Generates Q&A, tutorials, notes, practice questions from documents
- Uses local embeddings for cost efficiency
- Built TCP-based file transfer system with MD5 integrity checks
- Supports multi-file transfer over WiFi
- Faster than Bluetooth (~25%)
I enjoy building systems where AI meets real-world impact — from productivity tools to safety-focused applications.
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