Puneet
Kharagpur, Medinipur Division, West Bengal, India
Experience: 1 years
Open to: Full-Time
Education: Bachelors
Availability: Within 30 Days
Skills: C, CPP, Matplotlib, MySQL, NumPy, pandas, Python, Python3, PyTorch, scikit-learn, WordPress
Previously worked at: IT Service Companies, Startups
Assessment Score: 50
- Educational Background
Currently pursuing a B.Tech at the Indian Institute of Technology Kharagpur with a CGPA of 7.52 (expected graduation in 2025). Completed Class XII from D.A.V. LKB, Shimla with 87.4% and Class X from Monal Public School, Shimla with 91.8%. - Work Experience
Gained practical experience as a Machine Learning Engineer at Atomic Growth (Mar 2024 – May 2024), where they developed predictive models using algorithms such as Random Forest, Neural Networks, and XGBoost. Additionally, worked as an AI-Driven Mechanical Engineering Intern at Tech Japan (Nissin FULFIL Co. Ltd, Jun 2024 – Jul 2024), developing an anomaly detection algorithm to predict pump failures, achieving 92% accuracy. - Project Experience
Worked on advanced projects like the CFD Solver & Simulator using C++ with ML integration, predicting heat transfer coefficients with machine learning models, and Occupancy Prediction using Indoor Environmental Quality Data, where predictive models such as decision trees, Random Forest, and XGBoost were employed to enhance accuracy. - Technical Skills
Proficient in programming languages such as Python, C++, SQL, MATLAB, and JavaScript. Experienced in machine learning frameworks and libraries including Scikit-learn, TensorFlow, PyTorch, XGBoost, and Pandas. Well-versed in statistical modeling, data analysis, and preprocessing techniques like SMOTE and PCA. - Certifications and Specialized Training
Completed Stanford certifications in Advanced Learning Algorithms, Unsupervised Learning, and Reinforcement Learning, focusing on neural networks, decision trees, anomaly detection, and clustering techniques. Also, completed an OOPs course from Great Learning. - Hackathons and Competitions
Participated in the Bajaj Finserv National Hackathon 5.0, where they developed a text forgery detection system using transformer models, integrating visual and frequency perception heads to analyze text manipulation.