I am a Computer Science student with a strong passion for Artificial Intelligence, Machine Learning, and Backend Software Development, with a particular focus on Generative AI, Retrieval-Augmented Generation (RAG), Natural Language Processing (NLP), Full Stack Development, and Deep Learning.
I enjoy building applications that combine intelligent algorithms with scalable and well-structured software architectures to solve real-world problems and improve user experiences.
My current interests revolve around Generative AI systems, RAG pipelines, Deep Learning models, NLP applications, and full stack development using modern frameworks and tools, alongside backend systems built with Python.
I believe software engineering is not only about writing code but also about designing maintainable, efficient, and impactful solutions that can scale and adapt to real-world needs.
- 🤖 Developing AI-powered applications using Large Language Models
- 🔍 Exploring Retrieval-Augmented Generation (RAG) architectures
- 🐍 Building backend services with Python
- 📚 Learning software architecture and scalable system design
- 🚀 Continuously improving problem-solving and engineering skills
- Artificial Intelligence
- Machine Learning
- Generative AI
- Backend Engineering
- Natural Language Processing
- Software Architecture
- Data Engineering
An end-to-end Generative AI application implementing Retrieval-Augmented Generation (RAG) to answer user queries using contextual document retrieval and local language models.
- Semantic document retrieval
- Cross-encoder reranking
- Local LLM integration
- Context-aware question answering
- Modular AI pipeline
- Scalable architecture
A Python-based automation project that streamlines repetitive data analysis workflows, reducing manual effort and improving productivity through automated processing pipelines.
- Automated data cleaning
- Data transformation pipeline
- Report generation
- Workflow automation
- Python scripting
An end-to-end machine learning project demonstrating the complete ML lifecycle, including preprocessing, training, evaluation, and deployment-ready project organization.
- Data preprocessing
- Model training
- Performance evaluation
- Modular project structure
- Reproducible workflow
- Agentic AI Systems
- Large Language Models (LLMs)
- Retrieval-Augmented Generation (RAG)
- Backend Architecture
- Docker & Cloud Deployment
- Software Design Patterns