Available for AI/ML roles
I'm a B.Tech Artificial Intelligence & Machine Learning student and AWS × Udacity AI Engineer. I build FastAPI backends, train forecasting models, and ship agentic applications that hold up outside a notebook.
01 — About
I'm currently pursuing my B.Tech in Artificial Intelligence & Machine Learning at Vivekananda Institute of Professional Studies – Technical Campus (VIPS-TC), GGSIPU, in Delhi, India. With structured training as an AWS × Udacity AI Engineer, my core interest is building functional systems that combine predictive machine learning with natural language processing — designing streamlined FastAPI backends, building interactive dashboards with Streamlit, and engineering custom AI agents.
Present
B.Tech, Artificial Intelligence & Machine Learning
VIPS-TC, GGSIPU
AWS × Udacity
AWS Machine Learning Foundation Pathway
Udacity Foundation & AWS Academy
02 — Work
A registered patented system that forecasts employability metrics across G20 nations. Uses ARIMA and Prophet for multi-scenario forecasting, SHAP values for explainability, and a locally running Llama 3.2 analyst via Ollama.
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A Streamlit application analysing UPI QR codes and fintech news with Google's Gemini. Users upload a QR code to extract transaction metadata and get instant AI-powered legal compliance checks against local regulatory guidelines.
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A Devpost hackathon project mapping and forecasting salinity levels in ocean bodies worldwide, built with a custom Python ML pipeline and an agentic toolset that queries geographical sensors and computes oceanic patterns.
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A dual-objective exploratory workspace: unsupervised clustering (K-Means / Agglomerative) on Facebook live-seller engagement metrics, plus advertising sales forecasting with linear regression.
View repository ↗03 — Expertise
Designing autonomous agents with custom toolkits, wired into local or cloud LLMs to perform intelligent domain analysis.
Building scalable microservices with FastAPI and Python — async drivers, validation logic, and secure authorization standards.
Developing prediction engines with statistical methods (ARIMA) and structural models (Prophet) to model economic or seasonal indicators.
Deploying interactive dashboards with Streamlit — live-processed telemetry, charts, and AI-generated summary insights.
04 — Contact
I'm open to internships, job opportunities, research collaborations, or custom machine-learning systems. Drop me a line.