# Kavit Shah

> MS Data Science @ UCSD | Ex - Uniwise AI, Spyne AI | AI/ML, Data and Software | Full-Stack, Cloud and Scalable Systems

Profile: https://flows.cv/kavit

Hi, I’m Kavit.

Currently pursuing an MS in Data Science at UC San Diego, I focus on developing production-ready AI systems that integrate software engineering, machine learning, and cloud infrastructure. I have experience fine-tuning LLMs and Vision Models, building microservices, and deploying MLOps pipelines on AWS/GCP/Azure. I’ve developed multiple AI agents and implemented algorithms for ML and optimization, achieving measurable performance improvements. 

My passion lies in bridging the gap between cutting-edge AI research and real-world software systems, delivering solutions that are scalable, interpretable, and impactful.

## Work Experience
### Student Researcher @ UC San Diego
Jan 2025 | San Diego, California, United States
- Working on efficient LLM agents for advanced reasoning tasks, optimizing memory and inference workflows.
- Building modular AI pipelines for large-scale experiments and simulations using cloud and containerized infrastructure.
- Contributing to research advancing agentic AI architectures while ensuring scalable and reproducible methodologies.

### Quantitative Researcher @ Stealth Startup
Jan 2026 – Jan 2026

### Founding Engineer (SWE + ML) @ UniWise
Jan 2025 – Jan 2025 | San Diego, California, United States
- Built FastAPI-based backend microservices with Docker, optimizing data ingestion and real-time analytics workflows for scalable deployment.
- Integrated Google AI SDK to design and deploy AI agents for text and vision tasks, fine-tuning LLMs and Vision Transformers for compliant clinical and analytical use cases.
- Developed a full-stack MVP web application, implementing component-based architecture, client-side routing, responsive layouts, and dynamic API integration with backend ML services.
- Orchestrated MLOps pipelines for continuous training, deployment, and monitoring, improving system reliability and model performance.

### Applied AI Intern @ Spyne
Jan 2023 – Jan 2024 | Gurugram, Haryana, India
- Fine-tuned computer vision car classification models using state-of-the-art LLMs and CV architectures, addressing edge cases and increasing production accuracy from 76% → 98%, reducing misclassification rate by 74%.
- Engineered an automated model training pipeline with FastAPI backend, integrated Weights & Biases for model logging, resulting in a 25% reduction in training and processing time, tracking 100+ experiments seamlessly.
- Deployed models for real-time inference via Triton Inference Server, achieving <50ms latency per request and supporting 1000+ concurrent inference requests in production.
- Managed AWS cloud infrastructure, enabling scalable storage, distributed training, and fault-tolerant deployment, reducing downtime by 30% and improving model availability to 99.9%.

### Q-Machine Learning Intern @ BosonQ Psi (BQP)
Jan 2022 – Jan 2023 | Bengaluru, Karnataka, India
- Implemented quantum, quantum-inspired, and hybrid quantum-classical algorithms for large-scale machine learning, optimization, and simulation tasks, achieving up to 1.75× speedup in runtime and 20% improvement in solution accuracy compared to classical approaches.
- Developed and tested algorithms using Qiskit, Cirq, and PennyLane, running 50+ experiments on quantum simulators and near-term hardware, optimizing resource utilization and circuit depth by 15–25%.
- Evaluated algorithm scalability and performance, enabling hybrid pipelines to handle datasets with 10K+ samples and complex optimization problems efficiently.


## Education
### Master of Science - MS
UC San Diego

### Bachelor of Technology - BTech
Pandit Deendayal Energy University


## Contact & Social
- LinkedIn: https://linkedin.com/in/kavitjshah
- Email: mailto:kavitjshah@gmail.com

---
Source: https://flows.cv/kavit
JSON Resume: https://flows.cv/kavit/resume.json
Last updated: 2026-04-18