Machine Learning Engineer | GenAI & RAG Systems | ML Systems Builder
🎓 3rd-Year Data Science Student
📍 Pune, India
I build end-to-end Machine Learning systems — from data preprocessing to deployment.
My focus areas:
- Machine Learning pipelines & model development
- Retrieval-Augmented Generation (RAG) systems
- LLM-based applications
- Computer Vision for real-world problems
I focus on practical implementation, system design, and deployable solutions.
- Python, C++, SQL
- Pandas, NumPy
- Matplotlib, Seaborn
- EDA, Feature Engineering, Data Preprocessing
- Scikit-learn
- TensorFlow, PyTorch
- Model Evaluation & Optimization
- End-to-End ML Pipelines
- LangChain, LangGraph
- RAG (Retrieval-Augmented Generation)
- Prompt Engineering
- LLM Applications
- MLflow
- Docker (learning)
- Streamlit
- Git & GitHub
- Jupyter Notebook
ML Systems | MLOps | Real-Time Simulation
Built a digital twin system to simulate manufacturing workflows using ML models.
✔ Designed end-to-end ML pipelines with MLflow
✔ Implemented multiple models (Random Forest, XGBoost, Neural Networks)
✔ Integrated LLM-based alert system
✔ Deployed interactive Streamlit dashboard
👉 Focus: ML pipelines, system design, real-time monitoring
RAG | LLM Applications | Industrial AI
Developed an AI assistant for industrial troubleshooting using RAG.
✔ Integrated LLaMA3 with ChromaDB
✔ Built document retrieval + response generation pipeline
✔ Designed modular workflows using LangChain
✔ Context-aware Q&A over technical data
👉 Focus: RAG pipelines, LLM systems, applied GenAI
Computer Vision | Deep Learning
Built a CNN-based system to detect urban issues like potholes and garbage.
✔ Image classification using CNN
✔ Achieved 75% accuracy on validation data
✔ Applied data augmentation & tuning
✔ Designed backend detection logic
👉 Focus: Computer Vision, model optimization
Recommendation Systems | ML
Developed a content-based recommendation engine.
✔ Cosine similarity-based recommendations
✔ Feature engineering on movie metadata
✔ Built Streamlit interface for user interaction
👉 Focus: Recommendation systems, user-facing ML apps
- Docker for ML deployment
- FastAPI for model serving
- Advanced RAG architectures
- Scalable ML systems
✔ I build complete ML systems, not just models
✔ I focus on real-world applications
✔ I work across ML, GenAI, and deployment
✔ I aim for production-ready solutions
💼 Open to Machine Learning / Data Science / GenAI Internships