Third Year Computer Engineering Undergraduate | Data Science and Machine Learning | Java Full Stack Developer
I build systems that don’t just predict — they act, scale, and survive real-world constraints.
Most of my work sits at the intersection of:
Machine Learning | Real-time Systems | Retrieval-Augmented Intelligence (RAG) | Backend and APIs
I focus on building systems that are practical, scalable, and deployable in real-world environments.
My work involves:
- End-to-end machine learning pipelines: Moving beyond notebooks into production systems.
- Computer vision and deep learning systems: Specializing in YOLOv8, object detection, and transfer learning.
- Retrieval-augmented systems (RAG): Building smart, semantic-search-enabled systems for complex data.
- Backend systems for model deployment: Efficient model integration using modern APIs and microservices.
I don’t treat ML as isolated models. I build systems where Data flows → Processing → Model → API → Deployment → Feedback loop.
| Domain | Typical Tech Stack Snippets |
|---|---|
| ML and Data | PyTorch TensorFlow XGBoost Scikit-learn YOLOv8 Pandas NumPy Seaborn |
| CV and Deep Learning | CNNs Transfer Learning Image Classification Object Detection |
| RAG / NLP | FAISS Embeddings Sentence Transformers Semantic Search Vector DBs |
| Backend/APIs | FastAPI Flask Spring Boot REST APIs Microservices Architecture |
| Databases | MySQL PostgreSQL MongoDB SQLite Neo4j Apache Cassandra |
| Languages | Java Python C R SQL JavaScript TypeScript HTML CSS PHP |
| Tools | Git GitHub Postman Swagger Jupyter Google Cloud Figma QGIS |
- Speed and execution: Building systems that handle high throughput.
- Practical and deployable solutions: Ensuring code works beyond a local environment.
- Integration between ML and backend systems: Closing the gap between prediction and action.
- Reliability in real-world scenarios: Resilient architecture under pressure.
Most ML projects stop at prediction.
I focus on what happens after.
I’m interested in:
- Real-world AI systems: Systems that solve tangible, complex problems.
- High-speed decision systems: Where every millisecond count.
- Applied ML: Moving beyond experimentation into value delivery.
Let’s build.