Machine Learning Engineer

About the Role

Join Nexal IIT as a Machine Learning Intern on this remote internship program designed specifically for beginners and fresh graduates. Over a duration of 3 months, you will gain valuable exposure to data preprocessing, feature engineering, model development, training, evaluation, and deployment through real-world projects.

Your Responsibilities

• Collect, clean, preprocess, and prepare datasets for various machine learning initiatives.
• Conduct exploratory data analysis (EDA) to uncover patterns, trends, and relationships within the data.
• Develop, train, test, and evaluate ML models under mentorship from experienced professionals.
• Leverage Python libraries including NumPy, Pandas, Scikit-learn, Matplotlib, and Seaborn for analysis and visualization.
• Execute feature engineering, selection, and data transformation techniques to optimize model performance.
• Implement both supervised and unsupervised algorithms such as classification, regression, clustering, and recommendation systems.
• Evaluate model accuracy using appropriate metrics and iterate on parameters to enhance results.
• Assist in integrating trained models into applications or APIs where needed.
• Maintain detailed documentation of experiments, methodologies, code, and technical approaches.
• Stay updated with the latest ML techniques, frameworks, and industry trends by conducting research.

Your Requirements

We are looking for motivated individuals who:

• Are freshers or students interested in Machine Learning, AI, Data Science, Computer Science, or related fields.
• Have basic knowledge of Python programming and machine learning concepts (algorithms).
• Familiarity with Pandas, NumPy, Scikit-learn is highly desirable.
• Understand the fundamental math behind statistics, probability, and ML models.
• Know data preprocessing workflows and feature engineering basics.
• Are familiar with version control tools like Git.
• Are comfortable working in a remote environment independently while meeting deadlines responsibly.