I’m a Computer Science senior at UC Davis with deep expertise in building scalable, cloud-native AI systems. As a Software Engineering Intern, I’ve architected containerized, load-balanced pipelines using RAG-based LLMs on Azure and AWS. In my UC Davis research role, I led TinyML and Health-LLM initiatives—delivering a quantized ECG anomaly detector (IEEE ISCAS accepted) and a personalized Health LLM via fault-tolerant, event-driven data pipelines.

• AWS (EC2, S3, Lambda, ECS/EKS)
• Azure (VMs, Functions, AKS)
• GCP (Compute Engine, Cloud Functions)
• Terraform, CloudFormation
• Docker, Kubernetes, Serverless Architectures
• Load Balancing, CDN

• Core ML models, RAG-based LLMs, TinyML (edge-computing)
• PyTorch, TensorFlow, XGBoost, SHAP, LIME
• Apache NiFi, FHIR, Kafka
• GridSearchCV, Multilabel Classification
• Event-Driven Data Pipelines

• C#, .NET Core, Python (Flask, Django)
• Node.js, SQL (PostgreSQL, SQL Server)
• NoSQL (MongoDB), Prisma
• Microservices, RESTful & gRPC APIs
• CI/CD (GitHub Actions), Containerized Workflows

• Next.js, React, Tailwind CSS
• HTML5/CSS3, JavaScript/TypeScript
• Responsive UI, Vercel, Render

• Git & GitHub, Docker Compose, JIRA
• VS Code, PyCharm, Eclipse, Jupyter
• CI/CD Pipelines, Agile/Scrum Practices

I’m passionate about microservices, serverless architectures, and advanced data engineering to drive measurable impact—building robust ML pipelines, automating workflows, and deploying high-availability services. Let’s connect!

• 🔭 I’m currently working on InvestIQ AI, an AI-driven personal finance web application that offers predictive financial analysis.
• 🌱 I’m learning Rust, Deep Learning, Qiskit and Horse Riding
• 👯 I’m looking to collaborate on AI, machine learning, and finance tech projects.
• 💬 Ask me about Quantum Computing
• 📫 How to reach me: vsxpatel@ucdavis.edu

• Software Engineering Intern, Microsoft (through Drevol) (Oct 2024 – Present)

  • Automated software update testing workflows with a retrieval-augmented (RAG) LLM for log analysis, cutting error-triage time by 65% and improving resolution accuracy by 40%
  • Designed AI-driven pipelines for scheduled and ad-hoc software updates, increasing test throughput by 50% and accelerating release cycles by 25%
  • Engineered and refined automation solutions in C#, .NET, and SQL, increasing task efficiency by 20%
  • Developed and deployed AI solutions using image recognition and NLP (Natural Language Processing), reducing manual effort by 25%

• Machine Learning Research Assistant, UC Davis (Feb 2024 – Present)

  Project 2: Health LLM for Personalized Health Insights | Reinforcement learning, LLMs

  • Spearheaded development of a Health LLM that aggregates temporal, multi-dimensional health data—improving prediction precision by 92% and reducing response latency by 30%.
  • Engineered scalable data pipelines using Apache NiFi to convert raw health data into FHIR JSON schema, increasing processing throughput by 25% and enabling seamless integration from diverse sources.

  Project 1: TinyML for ECG Classification & Anomaly Detection

  • Engineered energy-efficient ECG classification using a quantized TinyML Random Forest (92.8% accuracy) and an event-driven architecture with adaptive burst-mode data collection, extending wearable battery life from 14 days to over a month.
  • Enhanced anomaly detection to 93.6% accuracy via advanced feature engineering and strategic hyperparameter tuning with GridSearchCV on resource-constrained devices.
  • Optimized deep learning for time-series data using SHAP and LIME, achieving a 7% accuracy boost and enabling hybrid offloading of complex multilabel classification to server-side CNN and XGBoost models for comprehensive health monitoring.

• CorpCred Visit | NextJS, TailwindCSS, Vercel, Render Oct 2024 – Jan 2025

  • Engineered a full-stack web app with a Random Forest model to predict corporate credit ratings, boosting accuracy by 15% and cutting latency by 30%

• InvestIQ AI Visit | Next.js, TailwindCSS, Vercel, NLP

  • Developed a predictive finance app with 90% accuracy, helping users optimize their portfolio performance by up to 15%.
  • Integrated an intelligent chatbot with a 95% query resolution rate and data visualizations that cut decision-making time by 30%.

• Dog Breed Classification Visit | Python, Render, MobileNetV2

• Scream Detection System | Python, MLP, Deep Learning, React

  • Designed a deep neural network to detect distress calls, achieving 93% accuracy by distinguishing acoustic differences.