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Job Title: AI/ML Intern Location: Remote Duration: 4 Months Type: Internship (Full-time) Stipend: Unpaid / No Stipend About the Role We are looking for a passionate and curious AI/ML Intern to join our team. You will work closely with our Data Science and Engineering teams to develop, train, and deploy machine learning models that solve real-world problems. This internship offers hands-on experience with modern AI tools, datasets, and production environments. Key Responsibilities - Assist in collecting, cleaning, and preprocessing structured and unstructured data. - Support the development, training, and evaluation of machine learning and deep learning models. - Research and implement algorithms for prediction, classification, and optimization tasks. - Collaborate with software engineers to integrate ML models into applications and APIs. - Perform exploratory data analysis (EDA) to extract actionable insights. - Document findings, experiments, and model performance. - Stay updated with the latest AI/ML research, frameworks, and best practices. Required Skills - Strong understanding of machine learning concepts, including supervised and unsupervised learning. - Experience with Python and libraries such as NumPy, Pandas, Scikit-learn, TensorFlow, or PyTorch. - Basic understanding of data preprocessing, feature engineering, and model evaluation. - Familiarity with Git/GitHub. - Strong analytical and problem-solving skills. - Ability to learn quickly and work collaboratively. Educational Qualification - Currently pursuing or recently completed a Bachelor's or Master's degree in Computer Science, Data Science, Artificial Intelligence, or a related field. What You Will Gain - Hands-on experience building and deploying AI/ML models. - Mentorship from experienced ML engineers and data scientists. - Chance to contribute to impactful AI projects. - Certificate of Completion and potential full-time opportunity based on performance. About Linkific Linkific is an emerging tech company building AI-driven automation platforms. We believe in turning failure into success through experimentation, bold risks, and continuous innovation. We value initiative, learning, and ownership above all. .