Machine Learning - Internship (WFH)

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

1. Machine Learning Model Development: Design, develop, train, and implement machine learning models using Python, ML algorithms, and modern frameworks.

2. Data Processing & Model Optimization: Collect, preprocess, and analyze datasets, evaluate model performance, and optimize algorithms for accuracy, efficiency, and scalability.

3. ML Integration & Deployment: Collaborate with development teams to integrate machine learning models into real-world applications, test solutions, and support model deployment.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 1st Dec'26 and 31st Dec'26are available for duration of 4 weekshave relevant skills and interestsStipend:INR₹ 5,000 - 6,000 /monthDeadline:[hidden]:59:59Other perks:Certificate, Letter of recommendation, Flexible work hoursSkills required:Python, Machine Learning and Deep LearningAbout Company: Our software company specializes in creating innovative, high-quality software solutions tailored to meet the needs of businesses across various industries. With a strong focus on user experience, scalability, and cutting-edge technology, we develop applications that drive efficiency and growth. Our team of skilled developers, designers, and marketers works collaboratively to deliver products that solve complex challenges and enhance productivity. Whether it's custom software development, mobile applications, or SaaS platforms, we are committed to excellence and customer satisfaction. Our company is dedicated to staying ahead of industry trends, ensuring that our clients receive the most advanced and effective solutions available.

Responsibilities

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

1. Machine Learning Model Development: Design, develop, train, and implement machine learning models using Python, ML algorithms, and modern frameworks.

2. Data Processing & Model Optimization: Collect, preprocess, and analyze datasets, evaluate model performance, and optimize algorithms for accuracy, efficiency, and scalability.

3. ML Integration & Deployment: Collaborate with development teams to integrate machine learning models into real-world applications, test solutions, and support model deployment.