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Rishabh Pal

@rishabhpal309

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AI/ML-focused data science enthusiast building production-ready ML applications and automation solutions.

India

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What I'm looking for

I seek roles where I can build production ML systems, work with agentic AI and analytics teams, and grow in software engineering and MLOps.

I am an AI/ML-focused data science enthusiast and developer currently pursuing a B.Tech, with hands-on experience building production-style projects that automate tasks and deliver analytics-driven insights.

I have developed agentic automation systems and sustainability analytics tools using Python, LangChain, Streamlit, Flask, and Scikit-learn, and built disaster-management and prediction pipelines using MongoDB and EM-DAT datasets.

I combine practical engineering (Docker, Git, cloud certifications) with strong visualization and analytics skills (Tableau, Power BI, Matplotlib) to create user-facing dashboards and interactive applications that drive decision-making.

Experience

Work history, roles, and key accomplishments

Current

Sustainability Analytics Developer

Current

Individual Project

Dec 2025 - Present (1 month)

Built a Carbon Footprint Optimization Engine using Python, Streamlit, and Scikit-learn to measure and predict personal carbon emissions and provide personalized reduction recommendations via interactive dashboards.

Python Streamlit scikit learn Regression Modeling Data Visualization Simulation

AI Automation Developer

Individual Project

Oct 2025 - Nov 2025 (1 month)

Developed TaskGenie, an AI-powered automation system using LangChain and CrewAI to autonomously plan and execute multi-step real-world tasks like flight search and content drafting. Designed an interactive Streamlit interface and integrated specialized agents and ChromaDB for task-specific automation.

Python Lang Chain Streamlit ChromaDB Agentic AI Crew AI NLP API Integration

Disaster Management Developer

Doom Meter

Mar 2025 - Apr 2025 (1 month)

Built Doom Meter, a Flask and MongoDB disaster management system using ML on 15+ years of EM-DAT data to predict disaster impacts and support responder decision-making. Improved API reliability through robust data validation and model structuring.