I’m an Applied AI & Data Science student at IIT Jodhpur with a strong curiosity for understanding how intelligent systems work and how they can be applied to meaningful real-world problems.

I enjoy learning → building → experimenting → understanding → improving, with a particular interest in AI systems that turn data and ideas into practical solutions.

I'm currently pursuing a B.S. in Applied AI and Data Science at IIT Jodhpur.

My interests span from foundational machine learning and deep learning to modern AI systems involving Generative AI, LLMs, RAG, and Agentic AI.

What interests me most is not just using AI tools, but understanding the ideas behind them and experimenting with how they can be used to solve real problems.

I'm continuously working on improving my foundations, building projects, exploring new technologies, and learning from both successful and failed experiments.

Curious learner → Builder → Problem Solver → AI Engineer / Researcher

• Machine Learning
• Deep Learning
• Neural Networks
• Model training & evaluation
• Feature engineering
• Experimentation & analysis

• Large Language Models (LLMs)
• Prompt Engineering
• Embeddings
• Retrieval-Augmented Generation (RAG)
• Vector search
• AI application development

• AI Agents
• Tool-using systems
• Agentic workflows
• Multi-step reasoning systems
• LLM-powered automation

• Data Analysis
• Data preprocessing
• Exploratory Data Analysis
• Statistical thinking
• Data-driven problem solving

• Natural Language Processing
• Computer Vision
• Intelligent automation
• AI-powered real-world applications

I'm currently going deeper into:

• Advanced Machine Learning
• Deep Learning & neural network architectures
• LLM architectures and practical applications
• RAG system design and evaluation
• AI Agents and agentic workflows
• AI Engineering & MLOps
• Research-oriented AI development
• Building reliable AI systems rather than just prototypes

I like working on projects where technology has a clear problem to solve.

My projects and experiments focus on:

• Real-world problem solving
• AI-powered applications
• Data-driven solutions
• Intelligent automation
• LLM & RAG applications
• Agentic AI workflows
• Machine Learning systems
• Practical AI experimentation

More projects will be added as I build, experiment, and learn.

Note: My stack is continuously evolving. Some technologies are areas I'm actively learning and experimenting with rather than claiming expert-level mastery.

I'm interested in exploring the intersection of AI research and practical engineering.

Areas I'm particularly interested in:

• Reading and understanding AI/ML research papers
• Reproducing interesting research ideas
• Experimenting with new AI techniques
• Contributing to open-source projects
• Building reproducible experiments
• Collaborating with developers and researchers
• Exploring research problems with real-world applications

I'm especially interested in opportunities where I can learn from experienced engineers/researchers while contributing meaningful work.

I don't want to learn technology only by collecting certificates or memorizing APIs.

My preferred approach is:

Learn → Build → Break → Understand → Improve → Repeat

Building something, discovering why it fails, and understanding the underlying concept is one of the ways I learn best.

My GitHub is where I document my learning journey through:

• Projects
• Experiments
• Implementations
• Research explorations
• Open-source contributions
• Technical notes

I'm always interested in connecting with people working on AI, Data Science, Machine Learning, research, open source, and interesting real-world problems.