Reasearch Intern AI

IBM’s roots trace back to Herman Hollerith’s 1896 invention of the tabulating machine, revolutionizing data processing for the 1890 US Census. In 1911, IBM was founded as the Computing-Tabulating-Recording Company, later renamed IBM in 1924. Key innovations followed, such as the 1928 introduction of the 80-column punched card, which became a global data standard.

IBM continues to push the boundaries of AI, quantum computing and sustainability. As a leader in ethical AI, IBM shapes transparent systems to address global challenges, from healthcare to environmental sustainability. The company also aims to drive innovation through partnerships in space technology, cloud computing and data-driven industries.

1. Summer Internship Program is one of the major activities under the University Relation Program. IBM Research India offers Summer Internship to the bright students of leading technical Universities in India.
2. IBM Research India provides industry experience and world class technologies to these students to show their talent. During internship students work on current projects happening in the lab, they solve problems with the help of their mentors.
3. Idea is to give industry experience to the students and look for future employees.

1. Excellent coding skills in Python (including Pandas, NumPy)
2. Strong grasp of Data Structures, Algorithms, Problem Solving
3. Solid foundation in Linear Algebra, Probability, and Statistics
4. Hands-on experience with VS Code, Jupyter Notebooks, Git
5. AI/ML Fundamentals: supervised & unsupervised learning, classification, regression, neural networks, clustering
6. Learning fundamentals and Data Manipulation/Analysis for large datasets
7. Proficiency in AI libraries/frameworks: TensorFlow, PyTorch
8. Experience in Deep Learning, Foundation Models, Large Language Models
9. Familiar with Generative AI frameworks such as langchain and crew.ai
10. Expertise in unstructured data processing
11. Experience working with platforms such as Watsonx.ai or similar
12. Familiarity with risks associated with AI deployments, including agents going rogue