Brijesh Kumar

A full-stack career platform utilizing RAG and vector embeddings to deliver personalized resume-job matching, increasing application rates by 40%. Engineered resilient microservices for auth and search handling 180 RPS with zero errors, and implemented automated Java web scraping to expand job databases by 3x.

A high-performance recommendation engine using CLIP and ResNet50 for vector-search, boosting user engagement by 32%. Features a real-time React+TypeScript swipe UI backed by an optimized FastAPI service with ANN, achieving sub-150ms p95 latency for 10k+ users with 99.99% uptime.

An intelligent contact management system leveraging Spring Boot and Kafka for microservices orchestration. Integrated AWS Bedrock and FAISS for RAG-based contact enrichment, raising match accuracy by 70%. Optimized with Redis read-through caching to deliver sub-3s network summaries.

An AI-powered learning assistant that transforms dense concepts into short, dialogue-based explainer videos with quizzes, streaks, and badges. If a student doesn’t understand a concept, SlideSage instantly regenerates fresh analogies until it clicks — making learning adaptive, fun, and addictive.

A hybrid RAG healthcare system combining semantic and keyword search with Sentence-BERT, improving top-k precision by 75%. Deployed a local LLaMA2 model with strict context-only guardrails to achieve 100% test-query accuracy, served via a robust Flask API on AWS ECS.

An end-to-end data pipeline ingesting tracks via Spotify API, automating ETL to reduce manual prep by 80%. Utilized Tableau and Python for EDA to identify viral trends and trained Scikit-learn models predicting playlist inclusion with 0.92 R2 score.