AI/ML - Internship (WFH)

About the internship:Selected intern's day-to-day responsibilities include:

1. Work on practical AI/ML projects using real-world datasets.

2. Perform data preprocessing, exploratory data analysis, and feature engineering.

3. Build, train, test, and evaluate machine learning models.

4. Apply supervised and unsupervised learning techniques to solve practical problems.

5. Optimize model performance and document project findings and results.Who can apply:Only those candidates can apply who:are available for the work from home job/internshipcan start the work from home job/internship between 5th Oct'26 and 9th Nov'26are available for duration of 2 monthshave relevant skills and interests* Women wanting to start/restart their career can also apply.Stipend:INR₹ 8,000 - 25,000 /monthDeadline:[hidden]:59:59Other perks:Certificate, Letter of recommendation, Flexible work hours, 5 days a weekSkills required:Python, Machine Learning, Artificial intelligence and NumPyOther Requirements:1. Freshers and students pursuing Computer Science, Data Science, AI/ML, Statistics, Mathematics, or related fields can apply.2. Basic knowledge of Python, Pandas, NumPy, and Scikit-learn is preferred.3. Candidates with a strong interest in Artificial Intelligence and Machine Learning are encouraged to apply.About Company:Opalcode is a technology-driven organization focused on building practical digital solutions while creating opportunities for emerging talent. We work across areas such as software development, data analytics, artificial intelligence, cybersecurity, and business technology. Our approach emphasizes hands-on projects, industry-oriented exposure, and continuous skill development, enabling aspiring professionals to gain practical experience and contribute to real-world technology initiatives.Additional information:

Stipend Structure:

Fixed pay: ₹ 5,000 - 20,000 /month

Incentive pay: ₹ 3,000 - 5,000 /month

Responsibilities

Selected intern's day-to-day responsibilities include:

1. Work on practical AI/ML projects using real-world datasets.

2. Perform data preprocessing, exploratory data analysis, and feature engineering.

3. Build, train, test, and evaluate machine learning models.

4. Apply supervised and unsupervised learning techniques to solve practical problems.

5. Optimize model performance and document project findings and results.