Machine Learning Intern

Machine Learning Intern

Inficore Soft

Location: Remote

Type: Full-Time Internship

Stipend: 18,000/month

Duration: 13 Months

Inficore Soft is looking for a Machine Learning Intern who is interested in building intelligent systems and working with real-world data. This role offers hands-on experience in developing, training, and evaluating machine learning models to solve practical business problems.

Key Responsibilities

Prepare and preprocess datasets for machine learning tasks

Build, train, and test machine learning models

Work on supervised and basic unsupervised learning techniques

Perform feature engineering and improve model accuracy

Analyze model performance and refine algorithms

Collaborate with teams to understand use cases and implement ML solutions

Work with Python libraries such as scikit-learn, Pandas, and NumPy

Document models, workflows, and results

Required Skills

Basic knowledge of Python programming

Understanding of machine learning algorithms and concepts

Familiarity with libraries like scikit-learn, Pandas, and NumPy

Basic knowledge of statistics and probability

Understanding of data preprocessing and model evaluation

Strong analytical and problem-solving skills

Attention to detail and ability to work with complex data

Ability to work independently in a remote environment

Eligibility Criteria

Students or recent graduates from Computer Science, AI, Data Science, or related fields

Freshers with basic machine learning knowledge are encouraged to apply

Candidates with academic or personal ML projects will have an advantage

Must be available for a full-time remote internship

Perks and Benefits

Stipend of 18,000 per month

Hands-on experience with real-world machine learning projects

Internship completion certificate

Opportunity to build a strong ML portfolio

Exposure to modern tools and industry practices

Learning-focused and growth-oriented environment

This internship is ideal for individuals who want to start their career in machine learning and gain practical experience in building data-driven intelligent solutions.