Machine Learning Internship

As an intern, your role will involve the following day-to-day responsibilities: - Research, identify, and understand business requirements to identify opportunities for applying predictive modeling and machine learning techniques. - Collaborate with cross-functional teams, including data scientists, software engineers, and domain experts, to define project objectives and requirements. - Design and implement predictive modeling and machine learning solutions, including data preprocessing, feature engineering, and model selection. - Develop and deploy predictive software applications into production, leveraging Python (or similar scripting languages) for efficient development. - Demonstrate expertise in training and fine-tuning LLMs on diverse datasets, ensuring superior performance, and adapting models for specific applications and domains. - Work with cloud-native solutions on platforms such as AWS, GCP, or Azure for scalability and flexibility. - Apply a range of machine learning algorithms for different tasks, including classification (SVM, decision tree, random forest, neural network), regression (linear, polynomial, logistic, etc), and graph theory (network analytics). - Utilize classical optimization techniques such as gradient descent and others for model optimization and parameter tuning. - Apply deep learning techniques, including LSTM, convolutional neural networks (CNN), and recurrent neural networks (RNN), for tasks such as natural language processing, image recognition, or time series analysis. - Experience with training ML models using Pytorch or tensorflow. The company you will be working for delivers business growth by providing AI-powered data enrichment and is currently operating in stealth mode. As an intern, your role will involve the following day-to-day responsibilities: - Research, identify, and understand business requirements to identify opportunities for applying predictive modeling and machine learning techniques. - Collaborate with cross-functional teams, including data scientists, software engineers, and domain experts, to define project objectives and requirements. - Design and implement predictive modeling and machine learning solutions, including data preprocessing, feature engineering, and model selection. - Develop and deploy predictive software applications into production, leveraging Python (or similar scripting languages) for efficient development. - Demonstrate expertise in training and fine-tuning LLMs on diverse datasets, ensuring superior performance, and adapting models for specific applications and domains. - Work with cloud-native solutions on platforms such as AWS, GCP, or Azure for scalability and flexibility. - Apply a range of machine learning algorithms for different tasks, including classification (SVM, decision tree, random forest, neural network), regression (linear, polynomial, logistic, etc), and graph theory (network analytics). - Utilize classical optimization techniques such as gradient descent and others for model optimization and parameter tuning. - Apply deep learning techniques, including LSTM, convolutional neural networks (CNN), and recurrent neural networks (RNN), for tasks such as natural language processing, image recognition, or time series analysis. - Experience with training ML models using Pytorch or tensorflow. The company you will be working for delivers business growth by providing AI-powered data enrichment and is currently operating in stealth mode.