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REQUIREMENT_ID_1462 • 3-DAY_ACTIVE_POLICY

AIML Incubator Internship

Company
Company Iqvia
Type
Opportunity Type Internship
Salary
Stipend / Salary Rs 15,000 per month
Location
Location Kochi, India
Posted Date
Posted Date Yesterday
Python Machine Learning Deep Learning PyTorch TensorFlow Prompt Engineering Cloud Platforms (Azure AWS GCP) Git Data Structures Problem Solving
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Aptitude and Reasoning Practice Open Resource ↗
Helps you sharpen quantitative and logical reasoning skills commonly tested in the first online assessment round.
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Technical Interview Preparation Guide Open Resource ↗
Covers core computer‑science concepts, coding patterns and AI/ML topics that are frequently asked during technical interviews.
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Comprehensive Interview Resources Open Resource ↗
Provides curated interview experiences, sample questions and tips specific to the Indian job market, useful for HR and technical rounds.
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Algorithmic Problem‑Solving Platform Open Resource ↗
Offers a large collection of coding problems to practice data structures and algorithms, essential for the coding assessment.

• Undergraduate (B.Tech/B.E) or postgraduate (M.Tech/M.Sc) students in Computer Science, Electronics, Information Technology or related streams. • Minimum CGPA/percentage of 60% (or equivalent). • Batch graduating between 2024‑2027. • No active backlogs at the time of application. • Strong interest in AI/ML, generative models and cloud technologies.

1
Round 1: Online aptitude & coding assessment
2
Round 2: Technical interview (AI/ML concepts, projects, coding)
3
Round 3: HR interview (fit, communication, motivation)
IQVIA is a global leader in clinical research services, commercial insights and healthcare intelligence. With a presence in more than 100 countries, the company helps life‑science organisations accelerate drug development, improve patient outcomes and drive market success. In India, IQVIA combines deep domain expertise with cutting‑edge data analytics to support pharma, biotech and healthcare providers. The firm’s culture is built around curiosity, collaboration and a relentless focus on innovation, making it an attractive destination for fresh talent eager to make a real impact in the health‑tech space. The AIML Incubator Internship is a 12‑month full‑time program based out of the Kochi office. Interns will work on real‑world problems using generative AI, large language models (LLMs), diffusion models and audio/video synthesis tools. The role is designed for self‑motivated students who love experimenting with the latest AI research, building prototypes, and delivering end‑to‑end solutions that can be deployed on cloud platforms. Throughout the internship, participants will receive mentorship from senior data scientists and have the opportunity to present their work to cross‑functional teams. **Key Responsibilities** 1. Conduct research on state‑of‑the‑art generative AI models and evaluate their suitability for business use cases. 2. Build and fine‑tune LLMs, diffusion models, and multimodal synthesis pipelines. 3. Design Retrieval‑Augmented Generation (RAG) workflows to improve contextual relevance of model outputs. 4. Apply prompt‑engineering techniques to optimise model behaviour across diverse tasks. 5. Develop scalable AI‑driven solutions and integrate them with existing business processes. 6. Create end‑to‑end deployment pipelines using Azure, AWS or GCP. 7. Benchmark AI/ML services across major cloud providers for performance, cost and scalability. 8. Collaborate with product owners, engineers and domain experts to validate prototypes. 9. Document experiments, results and best practices for knowledge sharing. 10. Present demos and technical findings to senior leadership. **Tech Stack**: Python, PyTorch, TensorFlow, LangChain, Azure AI Services, AWS SageMaker, GCP Vertex AI, Docker, Kubernetes, Git, REST APIs. **Growth Path**: High‑performing interns may be offered a full‑time role as an AI Engineer, Data Scientist or Research Engineer. The program provides exposure to end‑to‑end AI product development, positioning candidates for rapid career progression within IQVIA’s global AI labs. **Why Join IQVIA?** The internship offers hands‑on experience with cutting‑edge generative AI, mentorship from industry experts, and the chance to contribute to healthcare innovations that affect millions of lives. The collaborative Kochi office fosters in‑person learning, while the company’s strong emphasis on continuous upskilling ensures interns stay ahead of the technology curve.

Iqvia — 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 🎯
Iqvia Interview Preparation Corner

Comprehensive guide covering typical interview formats, common questions, and preparation tips for Iqvia 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 (Python, Machine Learning, Deep Learning, PyTorch, TensorFlow, Prompt Engineering, Cloud Platforms (Azure, AWS, GCP), Git, Data Structures, Problem Solving) & 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
Iqvia 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 Iqvia as a AIML Incubator Internship?
Preparation Tip: Highlight Iqvia's market reputation, recent tech innovations, and how your skills in Python 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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