About Me
AI Engineer / Data Scientist with 1 year of experience building and deploying production AI systems across LLM applications, RAG, NLP, speech analytics, and conversation intelligence. Experienced in developing scalable AI microservices, asynchronous processing pipelines, and semantic retrieval systems using Python, FastAPI, RabbitMQ, MongoDB, Vertex AI, and Docker.
Skills & Technologies
Work Experience
Data Scientist
Developing and deploying end-to-end AI systems focusing on LLM applications, RAG, speech analytics, and scalable AI pipelines. Built and deployed solutions using Python, FastAPI, RabbitMQ, MongoDB, NLP, LLMs, Whisper, VAD, FFmpeg, Gemini, LangGraph, Milvus, SentenceTransformers, Vertex AI, Flask, PostgreSQL/pgvector, Redis, Docker, Kubernetes, and CI/CD. Designed asynchronous pipelines handling over 10,000 conversation recordings monthly and performed evaluation of LLM and RAG models to ensure quality and performance.
Projects
Skin Disease Detection (CNN)
Achieved 96% accuracy on a large dermatology dataset and reduced false negatives by 15% through targeted model improvements. Deployed a real-time inference system via Flask and Docker with an image preprocessing pipeline that cut data processing time by 40%.
AI Assessment Recommendation System
Built an LLM-powered recommendation engine that analyzes responses with vector similarity to produce accurate assessment suggestions in real time. Integrated front-end with HTML/CSS/JavaScript and deployed on Google Cloud for scalable, low-latency performance.
Education
B.Tech
Computer Science & Engineering
Diploma
Automobile Engineering