Final-year CS student building production-grade backend systems at Morgan Stanley — distributed data pipelines, GenAI tooling, and developer productivity platforms.
- Currently at Morgan Stanley: Distributed data pipelines processing 50K+ logs/hour, GenAI automation cutting manual effort by 40%
- Latest project: JetRecon — vectorized reconciliation engine handling 50M+ rows with O(1) matching complexity
- Problem Solving: 360+ DSA problems on LeetCode · Top 25% globally
- Open to: Full-time Software Engineering roles from mid-2026
| Domain | Technologies |
|---|---|
| Languages | Python, TypeScript, SQL |
| Backend | FastAPI, Node.js, Celery, Pydantic, SQLAlchemy |
| GenAI | AI Agents, MCP, LLM Integration, Prompt Engineering |
| Distributed Pipelines | RabbitMQ, Celery, Python asyncio |
| Database | PostgreSQL, MongoDB |
| Infrastructure | Docker, Docker Compose, Linux, Git |
| CS Fundamentals | DSA, OOP, OS, DBMS, Computer Networks |
A vectorized data reconciliation engine built for scale.
- Engine: FastAPI + Polars achieving O(1) matching via hashing
- Scale: Out-of-core processing for 50M+ rows
- Infrastructure: Distributed pipeline with RabbitMQ, Celery, Docker Compose
A GenAI-powered automated grading platform for coding and academic submissions.
- Impact: Reduced evaluation time by 95% by automating multimodal submission review (video, image, text, audio) that previously required full manual review
- Features: Customizable rubrics, GenAI support chatbot
- Stack: Node.js, TypeScript, MongoDB