Im a final-year B.Tech student in Electronics and Computer Engineering at SRM Institute of Science and Technology, exploring the intersection of AI/ML, software engineering, and embedded systems. I love building end-to-end systems — from training neural networks and deploying REST APIs, to wiring up microcontrollers and making hardware talk to software. I'm curious about how intelligent systems learn, how reliable systems are engineered, and how the two can come together to solve real-world problems. At heart, I'm a builder who learns by doing — turning ideas into working prototypes, one project at a time.
- 🧠 AI/ML engineer-in-the-making, exploring Deep Learning, NLP, and Computer Vision
- 🎓 B.Tech ECE @ SRM (SGPA 9.38) | Final year — graduating May 2027
- 💼 Currently: Data Engineering Track @ PwC Launchpad (Advisory Learning Program)
- 🔬 Past: In-Plant Trainee in Embedded Systems @ IGCAR (Govt. of India's premier nuclear research center)
- 🏆 National Semi-Finalist — Flipkart Grid 7.0 | Top 20 — Google Cloud Gen AI Study Jam
- ☁️ AWS Certified AI Practitioner
- 👯 Looking to collaborate on open-source AI/ML and RAG projects
- 💬 Ask me about Python, TensorFlow, FastAPI, BERT, or Arduino
- 📫 Reach me at krithi11505@gmail.com
- 🎯 Goal: to build intelligent, reliable, and scalable systems that bridge AI and the real world
- Building RAG (Retrieval-Augmented Generation) projects — experimenting with vector databases, embeddings, and LLM-powered retrieval pipelines
- Designing enterprise-grade data pipelines and ETL workflows as part of the PwC Launchpad Data Engineering Track
- Machine Learning — deepening my fundamentals, implementing core algorithms from scratch, and understanding the math behind the models
- Cloud-based ML deployment and MLOps best practices
Languages
ML / AI
Backend & APIs
Databases & Cloud
Tools
- Heart Disease Risk Predictor — End-to-end neural network on UCI Cleveland + Statlog datasets. 91% accuracy, 0.97 ROC-AUC. Built with TensorFlow/Keras + FastAPI.
- Text-Based Emotion Detector — NLP system classifying emotions from text using fine-tuned BERT via Hugging Face Transformers.
"Built with ☕ and code."