→ Pre-final year @ VIT Vellore · M.Sc. Computational Statistics & Data Analytics · 9.23 GPA
→ Building production ML systems — not notebooks, not demos
→ Quantitative finance: portfolio optimisation, market microstructure, risk modelling
→ Agentic AI: LangGraph systems, autonomous workflows
→ Data engineering: distributed pipelines with Spark, Kafka, Hadoop on AWS
→ 546 DSA problems solved across platforms
→ 2 conference papers · 1 research internship (IIIT Allahabad)
→ Available immediately (remote) · Mid-May (on-site)
| Platform | Total | 🟢 Easy | 🟡 Medium | 🔴 Hard | Link |
|---|---|---|---|---|---|
| LeetCode | ↗ | ||||
| TakeUForward | ↗ | ||||
| GeeksForGeeks | ↗ | ||||
| ✨ Combined | — | — | — | — |
| Sheet | Progress | % |
|---|---|---|
| 📘 A2Z Sheet | 74% | |
| 🔁 DSA Concept Revision | 73% | |
| ⚡ DSA Quick Revision | 63% | |
| 👁 Blind 75 | 48% | |
| 🎯 Striver 79 | 68% | |
| 🗂 SDE Sheet | 47% |
| Topic | % | Mastery |
|---|---|---|
| 🔗 Linked Lists | 100% | |
| 🌲 Trees | 91% | |
| 🕸 Graphs | 89% | |
| 🔍 Binary Search | 85% | |
| 🧮 Dynamic Programming | 27% |
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Fully agentic job application pipeline. Parses JDs with NLP-based NER, retrieves CV sections via RAG, scores fit with sentence transformers, tailors LaTeX resumes, drafts emails — fires applications with a single Telegram tap. Self-improving match scorer retrains on personal outcomes. |
Distributed pipeline to detect wash trading, spoofing, and pump-and-dump in live Binance data. Fault-tolerant ETL on AWS EMR with idempotent jobs, dead-letter queues, and real-time dashboard on EC2. |
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Per-ticker LSTM forecasting with FinBERT sentiment analysis and Monte Carlo Sharpe ratio optimisation. Backtested across 10 stocks — sentiment-augmented model validated with paired t-test and Wilcoxon testing. Presented at 26th SET Conference. |
Self-pruning network with learnable sigmoid gates and L1 sparsity regularization on CIFAR-10. Achieves 277× compression — 99.6% of weights pruned while improving accuracy by 2% over baseline. Custom |