Artificial Intelligence & Machine Learning Student
I work across domains. I've built serverless data pipelines, competed in ML competitions under strict mathematical constraints, and shipped infrastructure that holds up under real traffic.
Currently open for remote engineering internships.
Technical Skills
LanguagesPython, C++, TypeScript, Java, SQL
AI & Data EngineeringPyTorch, TensorFlow, XGBoost, CatBoost, Polars, DuckDB, Transformers
Cloud & DevOpsAWS (Lambda, S3, EC2, SQS, CloudFront), Docker, Linux, Git
Backend & DatabasesFastAPI, Flask, DynamoDB, MongoDB
FrontendReact, Next.js, Tailwind CSS
Projects
§1. High-Constraint Machine Learning
Loss optimization, massive datasets, strict mathematical constraints.
ISRO Air Quality Forecasting[SIH 2025 Winner] — Physics-informed ML pipeline with 200+ features derived from atmospheric chemistry, correcting ISRO's raw CTM forecasts for NO₂ and O₃.
Alpha Radar[2nd Place / 150+ teams] — TCN and Transformer encoders for tick-level Solana momentum, fed into CatBoost under a strict 0.75 recall constraint.
Mallorn Classification — Hierarchical XGBoost pipeline with Hard Negative Mining to detect rare Tidal Disruption Events (5% of data) from astronomical light curves.
§2. Cloud and Serverless Infrastructure
Backends that hold up under real traffic.
Judgement Day — Event-driven document audit pipeline. Presigned S3 uploads and SQS decoupling handle scale without timeouts.
Blue Intelligence — Natural language interface over 4M+ oceanographic profiles. DuckDB queries an S3 data lake directly, and AI executes analysis code in an AST-validated sandbox.
§3. Developer Tooling
Tools that make other engineers faster.
IIC Reviews — Fully client-side React SPA for AI code reviews. In-browser key vault with automatic API key rotation handles rate limits without any backend.
QuantFeat — PyPI package implementing range-based volatility estimators (Parkinson, Garman-Klass, Yang-Zhang) and EDA utilities for quant research.
Experience
ML InternSimpragma Solutions
Nov 2024 - May 2025
- Engineered scalable Python backend and optimized LLM deployment, reducing inference latency by 60% via paged attention.
- Aligned model outputs with client requirements across real-world deployments.
Head of MarketingInnovation Cell, BMSIT
Aug 2024 - Present
- Directed nationwide campaigns securing 1000+ registrations for flagship events.
- Evaluated Round 1 proposals for national prototype competitions.
Chess TutorSNP SNK Classes
May 2024 - Jan 2025
- Mentored students in chess strategy and tournament preparation across multiple skill levels.
Certifications
Machine Learning Specialization[view]
Prof. Andrew Ng, Stanford University, Coursera
Deep Learning Specialization[view]
Prof. Andrew Ng, Stanford University, Coursera
Financial Markets
Prof. Robert Shiller, Yale University, Coursera
Education
BMS Institute of Technology
B.Tech in AI & ML2023 - 2027
Coursework: Data Structures, ML, Deep Learning
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