3rd-year AI & ML engineer at Marwadi University. I write code that survives being cloned documented architecture, honest metrics, tests that pass.
photo.png in /assets or use Preview mode ↘
Two years into writing code, and most of that time went into learning the difference between a working demo and a working system. Most student portfolios optimize for looking impressive. Mine optimizes for surviving scrutiny a recruiter who actually clones the repo, reads the README, and checks whether the numbers add up. That means synthetic data with documented provenance instead of unverifiable claims, incremental commit history instead of a single dropped-in blob, and architecture that matches what I call it a "multi-agent system" that actually passes data between agents, not four parallel one-shot calls wearing a label.
A full-stack DSA practice platform architecture docs, API contracts, database schema references, CI via GitHub Actions, graceful degradation when optional infra is down. 270+ incremental commits, not a single blob.
Multi-agent business-blueprint generator. Real DAG orchestration concurrent agents resolved via topological sort, writer agent consumes all upstream outputs. Grounded in live web search, not just LLM recall.
Employee burnout classifier on synthetic, provenance-documented data. Honest model selection with cross-validation, confusion matrix and per-class metrics in the README, 6 passing tests.
I am available for new opportunities and collaborations. Feel free to reach out via email, I check it daily.
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