Rohith
Hyderabad, Telangana, India
Experience: 4 years
Open to: Fractional, Full-Time, Part-Time
Education: Bachelors
Availability: Immediate
Skills: AWS, Azure, Blender, C, Docker, Git, Jenkins, Jira, Jupyter Notebooks, Kotlin, LLMs/ChatGPT, Looker, Matplotlib, MySQL, Notion, NumPy, pandas, Python, Python3, PyTorch, scikit-learn, TensorFlow
Previously worked at: Startups
Assessment Score: 100
- Experience
With hands-on experience in machine learning, data science, and AI, the candidate has worked on projects involving regression analysis, anomaly detection, and air quality prediction, utilizing supervised and unsupervised algorithms such as Random Forest, SVM, and XGBoost. The candidate also optimized model performance through techniques like hyperparameter tuning and feature engineering. - Education Background
Currently pursuing a Bachelor of Technology in Artificial Intelligence from Mahindra University, with a focus on Machine Learning, Deep Learning, and Natural Language Processing (NLP). The candidate has also developed custom transformer models for drug discovery and applied techniques like PEFT and LoRA for model optimization. - Project Experience
Led significant projects like the “Chemical Intelligence: Air Quality Prediction” project, which achieved 95.18% accuracy and earned a top-5 position at the IIT Kharagpur Fugacity Hackathon. Another key project involved Taxi Fare Price Prediction, where the candidate achieved the lowest RMSE of 18.3 using Gradient Boosting. - Hackathon Achievements
The candidate participated in and earned recognition at multiple hackathons, including the HSBC Hackathon (finalist) for an AI-powered fraud detection system, achieving 97% precision. They also placed in the top 25 in the IIIT Naya Raipur Data time Hackathon and the top 5 in the IIT Kharagpur Fugacity Hackathon. - Technical Expertise
Skilled in a variety of machine learning algorithms (e.g., Linear Regression, KMeans, SVM), deep learning architectures (e.g., CNN, RNN, LSTM), and NLP techniques. The candidate is proficient in Python, PyTorch, TensorFlow, and AWS, with experience deploying models for real-time applications. - Internship and Research Experience
As a Computer Vision Intern at Harvested Robotics, the candidate developed a testing framework for YOLO and RCNN models, achieving a 40% reduction in inference time for real-time applications. Additionally, they designed a custom Convolutional Autoencoder for industrial anomaly detection, achieving a 77.10% accuracy in classifying defects.