Focused on building LLM-driven systems, agent workflows, and semantic retrieval.
Career Objective: To obtain a challenging internship or entry-level position in back-end development, where I can apply my problem-solving sensibilities and coding skills to build accessible, user-centric interfaces while learning from experienced professionals.
I am experienced in implementing multi-step reasoning pipelines and state-aware systems using Python. Currently, I am exploring how flexible data models and vector search enable scalable intelligent agent memory.
Designed a multi-agent workflow system for handling complex, multi-step tasks. Implemented modular agent roles, state-aware task flows, and recursive self-correction mechanisms.
Developed a retrieval-based QA system using high-dimensional embedding similarity. Built a conversational recommendation system using sentiment-aware filtering in Python.
Developed an AI Movie Bot and actively participated in Kaggle competitions, ranking 33rd globally for the AI Study Companion project.
First Prize: Best Implementation (First-Year)
Built a quantum-classical hybrid solution using Qiskit for optimization tasks under strict time constraints.
| Qualification | Institution | Year | Performance |
|---|---|---|---|
| B.Tech (CSE - AI) | Amrita School of Computing | 2025 - 2029 | 7.42 SGPA |
| Higher Secondary (12th) | Placid Vidya Vihar | 2025 | 96.7% |
| Secondary (10th) | Aravinda Vidya Mandiram High School | 2023 | 89.0% |