Want to get hired at UM IT Solutions?
Machine Learning Intern
UM IT Solutions
HybridHybrid
Original Job Summary
About WebBoost Solutions by UM
WebBoost Solutions by UM provides students and graduates with hands-on learning and career growth opportunities in machine learning and data science.
Role Overview
As a Machine Learning Intern, you’ll work on real-world projects gaining practical experience in machine learning and data analysis.
Responsibilities
- Design, test, and optimize machine learning models.
- Analyze and preprocess datasets.
- Develop algorithms and predictive models for various applications.
- Use tools like TensorFlow, PyTorch, and Scikit-learn.
- Document findings and create reports to present insights.
Requirements
- Enrolled in or graduate of a relevant program (AI, ML, Data Science, Computer Science, or related field).
- Knowledge of machine learning concepts and algorithms.
- Proficiency in Python or R (preferred).
- Strong analytical and teamwork skills.
Benefits
- Stipend: ₹7,500 - ₹15,000 (Performance-Based)
- Practical machine learning experience.
- Internship Certificate & Letter of Recommendation.
- Build your portfolio with real-world projects.
How to Apply
Submit your application by 14th October 2025 with the subject: "Machine Learning Intern Application".
Equal Opportunity
WebBoost Solutions by UM is an equal opportunity employer, welcoming candidates from all backgrounds.
Key skills/competency
- Machine Learning
- Data Analysis
- Python
- TensorFlow
- PyTorch
- Scikit-learn
- Data Preprocessing
- Algorithm Development
- Teamwork
- Reporting
How to Get Hired at UM IT Solutions
🎯 Tips for Getting Hired
- Research WebBoost Solutions by UM: Understand their mission and recent projects.
- Customize your resume: Highlight relevant machine learning skills.
- Prepare project examples: Showcase hands-on experience in ML.
- Practice technical questions: Review Python and ML algorithms.
📝 Interview Preparation Advice
Technical Preparation
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Review Python programming basics.
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Study TensorFlow tutorials.
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Practice ML model design.
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Analyze sample datasets.
Behavioral Questions
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Describe teamwork in previous projects.
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Explain problem-solving under pressure.
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Share learning from project challenges.
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Discuss communication in collaborative settings.