About Me
I'm a recent Industrial Engineering graduate with a background in applied ML, looking for AI, ML, or data science roles where the work is connected to real decisions and real constraints. My path into ML didn't follow a standard CS track. I studied Chemical Engineering at NIT Bhopal, then came to the U.S. on a fully funded scholarship tied to the IE department. That structure shaped how I learned: my coursework was grounded in statistics, optimization, and systems thinking rather than pure computer science.
I built my ML skills through projects connected to real domains — energy, operations, and security — where model outputs directly affect costs and risk. That context changed how I think about the work. I spend as much time thinking about how a model might fail, drift, or be misused as I do about how to train it. I care about data quality, cost-aware evaluation, interpretability, and what happens after deployment, not just benchmark numbers.
Across my projects I've built end-to-end pipelines covering ingestion, feature engineering, training, evaluation, and production deployment. I've worked with both classical and modern ML tooling, and I try to make trade-offs explicit rather than defaulting to complexity. I'm most useful on teams where ML is embedded in a larger operational system and needs to be trusted and maintained over time.