Built and shipped core React Native features (guest lists, RSVPs, creator discovery) used by 50k+ users, improving mobile performance and navigation speed by 30% through optimized rendering.

Developed scalable Node.js backend APIs for high‑volume event workflows, reducing user drop‑off during event flow by 25% with improved validation and error handling.

Optimized database queries and implemented Redis caching for event search, reducing API response times from 800ms to 120ms (85% improvement) and supporting 500+ concurrent users.

Led infrastructure scaling and optimization, implementing advanced caching strategies and database tuning that reduced API latency by 40% under peak load.

Architected ML pipeline orchestration with Airflow and MLflow, automating model retraining and deployment workflows for continuous improvement.

Implemented comprehensive monitoring and alerting system using Prometheus and Grafana, achieving 99.9% uptime for production ML services.

Mentored junior engineers on best practices for ML infrastructure, DevOps workflows, and production system design.

Built clinical AI nutrition platform serving 200+ patients with subgraph DAG + RAG pipeline (LangChain, QDrant), improving dietary recommendation accuracy by 22%.

Collaborated with Food Network Chef James Briscione and White House Fellow Prof. Lav Varshney to integrate culinary expertise into AI recommendation engine.

Deployed HIPAA-compliant Next.js platform with GPT-4o integration, implementing encryption, audit logging, and secure PHI handling, passing UCSD Health IT security review.

Built agentic workflow with LangGraph for multi-step reasoning (medical history, allergies, meal generation), reducing manual dietician review time by 65%.

As first founding engineer, architected complete agentic e-commerce OS from ground up, scaling to 4+ enterprise clients processing $2M+ GMV. Helped raise $1.0M seed round from Intel Capital and UpHonest Capital.

Designed agent workflows processing 2,000+ daily queries with vector search (Pinecone) and adaptive ranking, achieving 92% relevance accuracy and <200ms query latency.

Built no-code framework reducing client onboarding time by 90%, enabling non-technical teams to deploy custom search experiences in <2 hours.

Optimized Postgres + Redis stack with connection pooling and intelligent caching, increasing throughput 4× (250 to 1,000 ops/sec) and cutting p95 latency by 30%.

Shipped Gemini multimodal search enabling conversational and visual product discovery, improving conversion rates by 40% for enterprise clients.

Built full-stack ETL dashboard processing 120k+ clinical trial reports, reducing document analysis time from 4 hours to 45 minutes per batch (80% reduction) with automated extraction.

Implemented semantic search using OpenAI embeddings and FAISS vector database, improving R&D team productivity by 3x for finding relevant trials and adverse event reports.

Built FastAPI backend with async processing for large-scale ingestion, implementing multi-threaded PDF parsing and OCR (Tesseract) for scanned documents.

Upgraded PostgreSQL + AWS S3 stack with database optimization and indexing, increasing throughput 4× (500 to 2,000 documents/hour) for R&D pipeline.

Modeled ion–electron Coulomb drag coupling to predict open‑circuit voltage and short‑circuit current in Si nanochannels.

Designed CNT‑integrated channel architectures targeting higher momentum transfer and energy harvesting efficiency.

Built subgraph DAG + RAG pipeline (LangChain, QDrant vector DB), improving dietary recommendation accuracy by 22%.

Collaborated with Food Network Chef James Briscione & White House Fellow Lav Varshney on clinical AI nutrition engine.

Built real‑time React + Flask platform for multi‑user LLM interaction research (40+ study subjects).

Ensured zero‑loss message reliability via WebSockets + analytics logging for human–AI interaction experiments.

Integrated RL agent into MPC framework for autonomous EV routing in SUMO → improved control accuracy 25%.

Achieved 18% lower traffic delay in open‑world simulations using ROS, C++, Python.

Analyzed experimental data and developed new concrete mixture by reducing clinker in cement.

Built Python + Random Forest models to predict composition impact on durability, reducing carbon emissions by 10%.

Held office hours and provided one-on-one support for students in computer science courses.

Graded assignments and provided detailed feedback to help students improve their programming skills.

Conducted market analysis and developed a go-to-market strategy for a new product line in the pipefitting industry.

Used NLP techniques to simplify complex insurance documents, making them more understandable for customers.

Developed and implemented a secure UART communication protocol for an embedded medical device.

Contributed to securing x86 architecture firmware by implementing memory-safe coding practices in Rust.

Built and styled responsive UI components for a cross-platform mobile app using React Native.

Translated Figma mockups into functional application screens and integrated them with a Python Flask backend.