MACHINE LEARNING INTERN

About the program EduRankAI invites applications for the position of Machine Learning Intern to join its Artificial Intelligence and Machine Learning Engineering team. This fulltime internship is designed for students and recent graduates who are passionate about Machine Learning, Artificial Intelligence, Data Science, Predictive Analytics, Statistical Modelling, and intelligent software systems. As a Machine Learning Intern, you will contribute to the development, evaluation, optimization, and deployment of machine learning models that power EduRankAI's AI Tutor, recommendation engines, personalized learning systems, educational analytics, intelligent automation, and research initiatives. You will work on realworld Machine Learning applications involving classification, regression, recommendation systems, ranking algorithms, forecasting, anomaly detection, and predictive modelling under the mentorship of experienced Machine Learning Engineers and AI Researchers. As a Machine Learning Intern, you will collaborate with AI researchers, machine learning engineers, data scientists, data engineers, software engineers, and product teams to build productionready machine learning solutions. Your responsibilities will include developing, training, and evaluating machine learning models for classification, regression, clustering, recommendation, ranking, and forecasting tasks performing data preprocessing, feature engineering, feature selection, and exploratory data analysis EDA implementing hyperparameter tuning, crossvalidation, and model optimization techniques analysing model performance using statistical evaluation metrics supporting deployment of machine learning models into development and staging environments integrating models with data pipelines and software applications maintaining reproducible machine learning experiments documenting methodologies, assumptions, and experimental findings collaborating with data engineering teams to improve data quality and model pipelines and contributing to continuous improvements in AIdriven educational products. Applicants should possess a strong understanding of Python programming, machine learning fundamentals, statistics, probability, linear algebra, data structures, algorithms, and software engineering principles. Familiarity with Scikitlearn, Pandas, NumPy, Matplotlib, Plotly, TensorFlow, PyTorch, SQL, Jupyter Notebooks, Git, Linux, feature engineering, model evaluation metrics, hyperparameter optimization, model deployment, experiment tracking tools such as MLflow or Weights Biases WB, cloudbased machine learning platforms, or MLOps concepts will be considered an advantage but is not mandatory. Knowledge of recommendation systems, ensemble learning, decision trees, random forests, gradient boosting, clustering, anomaly detection, Natural Language Processing NLP, Computer Vision, or Deep Learning will also be beneficial. Candidates with academic projects, hackathons, AI competitions, opensource contributions, research work, or personal Machine Learning projects are strongly encouraged to apply. Throughout the internship, participants will receive structured mentorship, continuous technical guidance, engineering best practices, research discussions, code reviews, and exposure to modern Machine Learning development workflows. Interns will gain practical experience in predictive modelling, feature engineering, model evaluation, experimentation, MLOps fundamentals, technical documentation, collaborative software development, and production Machine Learning systems while contributing directly to live Artificial Intelligence initiatives. We are looking for individuals with strong analytical ability, logical reasoning, mathematical aptitude, curiosity, creativity, engineering discipline, problemsolving skills, attention to detail, communication skills, ownership, teamwork, adaptability, research orientation, and a continuous learning mindset. The internship is primarily conducted onsite at the EduRankAI campus with structured mentorship and collaborative engineering activities. Remote participation may be permitted only in exceptional, preapproved cases. Applications submitted under EduRankAI's Talent Accessibility Initiative are completely free. This is an unpaid internship intended to provide meaningful industry exposure, structured mentorship, handson Machine Learning experience, professional portfolio development, and opportunities for outstanding performers to be considered for advanced AI engineering projects, internship extensions, preplacement interviews, research collaborations, or future fulltime opportunities based on performance and organizational requirements. Perks Certificate of Completion Letter of Recommendation for exceptional performers Mentorship from Senior Machine Learning Engineers and AI Researchers Hands-on experience with production-grade Machine Learning systems Exposure to predictive modelling, feature engineering, model optimization, and MLOps fundamentals Opportunity to contribute to live AI-powered educational products and research initiatives Professional Machine Learning portfolio development Cross-functional collaboration with AI research, data science, software engineering, and product teams Performance-based Pre-Placement Interview opportunity Exposure to modern Machine Learning engineering, data-driven decision making, and AI product development Who can apply? Only those candidates can apply who: are from Any and specialisation from Any are available for duration of 3 Months have relevant skills and interests Terms of Engagement This is a full-time internship requiring a commitment of eight hours per day, six days per week, for a duration of three months. The internship is primarily conducted on-site at the EduRankAI campus with structured mentorship and collaborative engineering activities. Remote participation may be permitted only in exceptional cases with prior organizational approval. Interns will contribute to live Machine Learning and Artificial Intelligence projects while maintaining professionalism, confidentiality, documentation standards, engineering best practices, research integrity, and project timelines. Participants will collaborate with multidisciplinary teams, develop and evaluate machine learning models, optimize predictive algorithms, integrate models into development pipelines, prepare technical documentation, participate in engineering reviews, and support continuous improvements in production-ready AI systems. Successful completion of the internship will be evaluated based on technical competency, model quality, analytical ability, engineering standards, documentation quality, collaboration, innovation, ownership, consistency, professionalism, and overall contribution throughout the internship. Number of openings 10