My Work

Built a full-stack race strategy simulator using Next.js, FastAPI, and Gemini AI, enabling real-time simulation, multi-strategy comparison, and weather-aware race prediction with interactive visualizations; deployed on AWS Lambda and Vercel, achieving 40% faster cold-start time through serverless optimization and AI inference rate-limiting.

A cloud-native microservices platform for real-time business recommendations using gRPC, Kafka, Redis, and Flask. Built scalable AI-first architecture with PostgreSQL, Kubernetes, and content-aware recommendation engine.

A full-stack web platform using LLMs to match resumes to jobs, tailor content with generative AI, and provide ATS-optimized resume outputs. Built with Django and React for a seamless user experience.

A machine learning project predicting F1 championship winners using clustering, PCA, and ML models. Uses data from the Ergast F1 API and explores driver and constructor patterns.

A real-time microservices platform that matches users to businesses using Kafka, Redis, FastAPI, and Streamlit. Built with containerized services and a live recommendation pipeline powered by streaming Yelp data.

A complete ML pipeline with fine-tuned BERT for binary sentiment classification. Includes PyTorch/TensorFlow support, FastAPI REST API, Prometheus monitoring, and Docker-based deployment.

A data visualization dashboard that explores Formula 1 racing statistics using Streamlit and embedded Tableau Public views. Provides real-time, interactive insights into race data, driver performance, and team analytics.