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Design Thinking with Artificial Intelligence Internship (6‑Week) – Oct

Company
Company IBM
Type
Opportunity Type Internship
Salary
Stipend / Salary Unpaid (no stipend)
Location
Location Pan India (Remote)
Posted Date
Posted Date Yesterday
Design Thinking Artificial Intelligence Machine Learning Deep Learning No‑code AI tools Problem Solving User Research Communication Presentation Collaboration
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Aptitude Practice Questions Open Resource ↗
Helps you sharpen quantitative and logical reasoning skills essential for the selection task and interview rounds.
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Company Interview Preparation Guide Open Resource ↗
Provides insights into typical interview formats, common questions, and answer strategies useful for the short conversation or take‑home task.
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Comprehensive Study Notes Open Resource ↗
Offers curated notes on AI fundamentals, design thinking, and no‑code tools to help you perform well during the internship sessions.
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Algorithm Problem Sets Open Resource ↗
Enables practice of coding logic and algorithmic thinking, which can be advantageous when building AI prototypes using low‑code platforms.

Open to students pursuing non‑technical education such as BA, BCom, BBA, MBA, Diploma, BSc, etc. Must have a valid Gmail account for SkillsBuild registration, be available for the full 1‑month duration, and possess a computer or laptop with stable internet connectivity. Preferred operating systems are Windows or Linux. An updated browser (Google Chrome or Firefox) is recommended. No minimum CGPA or back‑log restrictions are mentioned, but candidates should be able to commit to the schedule and complete the individual project.

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Round 1: Application submission
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Round 2: Application screening by Edunet Foundation
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Round 3: Shortlisted candidates receive a brief interview or take‑home task
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Round 4: Final selection and offer letter
Edunet Foundation, a nationally recognised non‑profit education platform, partners with IBM SkillsBuild to deliver cutting‑edge learning experiences for students across India. With a mission to democratise technology education, Edunet has built a robust ecosystem of virtual classrooms, industry mentors, and certification pathways that empower learners from non‑technical backgrounds to acquire future‑ready skills. The organisation is a verified partner on the National Internship Portal and enjoys AICTE endorsement, ensuring that every internship it offers meets stringent quality standards. The Design Thinking with Artificial Intelligence internship is a six‑week, fully remote program that blends human‑centred design methodology with practical AI implementation using no‑code/low‑code tools. Over the course of the internship, participants will engage in interactive live sessions, hands‑on workshops, and real‑world project work. The curriculum is structured to take a learner from understanding user needs, through ideation and prototyping, to building AI‑powered solutions that address tangible problems. By the end of the program, each intern will have delivered an individual AI‑driven prototype, earned a certificate of completion, and received a digital credential that can be showcased on professional profiles. Key responsibilities include: 1. Attend and actively participate in weekly virtual master‑class sessions led by industry experts. 2. Conduct user research through surveys, interviews, or secondary data analysis to identify pain points. 3. Apply Design Thinking stages – empathise, define, ideate, prototype, and test – to frame problem statements. 4. Leverage no‑code AI platforms (e.g., IBM Watson Studio, Google AutoML) to develop functional prototypes. 5. Document design decisions, data sources, and model performance in a project notebook. 6. Collaborate with peers in breakout rooms to brainstorm solutions and provide constructive feedback. 7. Prepare a concise project presentation covering problem, solution, AI workflow, and impact. 8. Submit the final project deliverables and receive mentorship feedback for improvement. The technical stack is intentionally low‑code: Google Colab notebooks, IBM SkillsBuild AI labs, drag‑and‑drop model builders, and basic data visualisation tools. Soft‑skill development is equally emphasized, with focus on communication, storytelling, and stakeholder management. Successful completion opens pathways to advanced AI certifications, internships with partner firms, or entry‑level roles in product design and AI consulting. Joining this internship offers a rare chance to blend creativity with technology, gain mentorship from seasoned professionals, and build a portfolio that stands out in a competitive job market.

IBM — AI & Machine Learning Interview Guide

Previously asked questions, exam syllabus, coding benchmarks & round strategy.

APTITUDE & LOGIC 📖
Aptitude Practice Questions & Online Mock Tests

Curated logical, quantitative, and verbal reasoning problems to sharpen reasoning skills required for the initial screening test.

Open Resource ↗
COMPANY GUIDE 🎯
IBM Interview Preparation Corner

Comprehensive guide covering typical interview formats, common questions, and preparation tips for IBM and off-campus tech roles.

Open Resource ↗
MOCK TESTS & PAPERS 📝
Comprehensive Interview Prep Resources & Syllabus

Collection of previous year questions, company-specific test patterns, and interview experiences for technical and HR rounds.

Open Resource ↗
CODING PRACTICE 💻
Algorithm and Data Structure Problem Set

Practice problems to improve coding proficiency and algorithm problem-solving speed for technical rounds.

Open Resource ↗
Round 1: Online Assessment (OA)
Aptitude, Quantitative Logic & 2 Coding Problems
Focus on accuracy and speed. Practice arrays, strings, and standard arithmetic puzzles.
Round 2: Technical Interview 1
Core Tech Stack (Design Thinking, Artificial Intelligence, Machine Learning, Deep Learning, No‑code AI tools, Problem Solving, User Research, Communication, Presentation, Collaboration) & Live Code Tracing
Explain your thought process aloud. Analyze time & space complexities before coding.
Round 3: System Design & Problem Solving
Database Schemas, APIs & Architecture Basics
Clarify edge cases, diagram schemas cleanly, and discuss scalability trade-offs.
Round 4: HR & Cultural Fit Discussion
IBM Core Values, Learning Agility & Offer Terms
Demonstrate passion, strong communication, and readiness for full-time collaboration.
What is the bias-variance tradeoff and how do you prevent overfitting? Answer ▼
Model Answer: High bias leads to underfitting (oversimplified model), high variance leads to overfitting (captures noise). Mitigate using L1/L2 Regularization, Dropout, Cross-Validation, and data augmentation.
Explain the difference between Precision, Recall, and F1-Score. Answer ▼
Model Answer: Precision = TP / (TP + FP) (correctness of positive predictions). Recall = TP / (TP + FN) (coverage of actual positives). F1-Score is the harmonic mean of Precision and Recall.
How does Gradient Descent work and what is the role of Learning Rate? Answer ▼
Model Answer: It optimizes loss functions by iteratively moving weights in the direction of negative gradient. A large learning rate may overshoot the minimum; a small rate causes slow convergence.
What is the difference between Supervised, Unsupervised, and Self-Supervised learning? Answer ▼
Model Answer: Supervised uses labeled data (X -> y). Unsupervised finds hidden patterns in unlabeled data (clustering/PCA). Self-supervised generates labels from input data (e.g. masked language modeling in BERT/Transformers).
Why do you want to join IBM as a Design Thinking with Artificial Intelligence Internship (6‑Week) – Oct?
Preparation Tip: Highlight IBM's market reputation, recent tech innovations, and how your skills in Design Thinking directly solve their team's objectives.
Describe a challenging bug or academic project roadblock and how you resolved it.
Preparation Tip: Use the STAR method: Situation (project context), Task (what needed solving), Action (specific tools/logic applied), Result (quantifiable positive outcome).
How do you handle strict deadlines or sudden scope changes?
Preparation Tip: Explain your prioritization strategy, proactive communication with mentors/peers, and agile mindset.

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