B.Tech in Artificial Intelligence & Data Science (2023–2027)
building machine learning, generative AI, and agentic systems with a focus on clarity, reliability, and real-world use.

I work at the intersection of machine learning, deep learning, and generative AI, with a strong interest in how models behave beyond accuracy metrics—
how they fail, how they reason, and how they can improve over time.

I prefer building systems from first principles before scaling them, so that design decisions are deliberate, testable, and explainable.

• understanding internals of transformers, LLMs, and retrieval systems
• building self-improving and tool-using AI agents
• designing end-to-end ML pipelines that are reproducible and deployable
• learning production trade-offs in ML and GenAI systems

• Machine Learning: supervised & unsupervised learning, feature engineering, evaluation, classical ML pipelines
• Deep Learning: neural networks, CNNs, sequence models, optimization
• NLP & GenAI: transformers, embeddings, RAG, prompt engineering
• Agentic AI: tool-using agents, memory-based systems, reasoning loops
• Deployment: FastAPI-based inference, lightweight MLOps practices

Build slowly. Understand deeply.
Clarity compounds faster than complexity.