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REQUIREMENT_ID_219 • 3-DAY_ACTIVE_POLICY

GenAI Trainee

Company
Company Larsen & Toubro (L&T)
Type
Opportunity Type Internship
Salary
Stipend / Salary 4.5 LPA+ Estimated
Location
Location Powai, Maharashtra
Posted Date
Posted Date Yesterday
Machine Learning Deep Learning Python TensorFlow PyTorch OpenCV Computer Vision Generative AI Large Language Models Data Preprocessing Model Deployment API Development Git
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Aptitude Practice Questions Open Resource ↗
Helps you sharpen quantitative and logical reasoning skills essential for the online test at L&T.
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Company Interview Preparation Guide Open Resource ↗
Provides insights into typical interview patterns and questions asked by L&T for technical roles.
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Comprehensive Interview Prep Resources Open Resource ↗
Covers coding, data‑structures and system design topics frequently tested in L&T technical rounds.
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Algorithm & Data Structure Problem Set Open Resource ↗
Offers a curated set of coding problems to practice for L&T's programming assessments.

Bachelor of Technology (B.Tech) or Master of Technology (M.Tech) in Computer Science, Electronics, Information Technology, Electrical Engineering or related streams; no active backlogs at the time of joining; strong foundation in programming, mathematics and basic machine‑learning concepts.

1
Round 1: Online aptitude & technical test
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Round 2: Technical interview (coding, ML concepts, project discussion)
3
Round 3: HR interview (fitment, communication, career goals)
Larsen & Toubro (L&T) is one of India’s largest engineering conglomerates, known for its diversified portfolio spanning infrastructure, heavy engineering, technology services and digital solutions. The company’s Precision Engineering & Systems division, based in Powai, focuses on cutting‑edge research and development in automation, robotics and artificial intelligence. With a strong emphasis on innovation, L&T invests heavily in building AI‑driven products that cater to sectors such as manufacturing, energy, and smart cities. The work culture encourages continuous learning, cross‑functional collaboration, and a merit‑based growth trajectory, making it an attractive destination for fresh engineering talent. The GenAI Trainee role is designed for recent B.Tech/M.Tech graduates who are eager to dive deep into the world of Machine Learning, Generative AI and Computer Vision. As a trainee, you will work alongside senior AI Engineers and Data Scientists, assisting in end‑to‑end AI solution development – from data collection and preprocessing to model deployment and monitoring. The position offers hands‑on exposure to industry‑grade AI frameworks, cloud platforms, and real‑time production pipelines, providing a solid foundation for a long‑term career in AI engineering. **Key Responsibilities** 1. Collect, cleanse, preprocess and validate structured and unstructured datasets for ML projects. 2. Support design, development, training and evaluation of machine‑learning and deep‑learning models. 3. Perform feature engineering, hyper‑parameter tuning and model performance optimisation. 4. Assist in model validation, benchmarking, documentation and deployment in development/production environments. 5. Contribute to Generative AI initiatives – prompt engineering, fine‑tuning LLMs, building RAG pipelines and AI‑powered chatbots. 6. Develop computer‑vision solutions including object detection, image classification, segmentation, tracking and OCR using OpenCV, TensorFlow, PyTorch and YOLO. 7. Conduct exploratory data analysis, create visualisations, dashboards and support data‑pipeline development. 8. Integrate AI models with web, mobile and enterprise applications via APIs, micro‑services and cloud services. 9. Participate in research, proof‑of‑concepts, and stay updated with emerging AI trends. 10. Prepare technical documentation, ensure compliance with Responsible AI and data‑privacy guidelines. **Tech Stack**: Python, Pandas, NumPy, Scikit‑learn, TensorFlow, PyTorch, OpenCV, YOLO, LangChain, FastAPI, Docker, Kubernetes, AWS/GCP, Git. **Growth Path**: Successful trainees can progress to AI Engineer, Senior AI Engineer, and eventually AI Lead or Solution Architect roles, with opportunities to lead product‑centric AI projects across L&T’s global business units. **Why Join L&T?** L&T offers a stable, well‑structured environment backed by a strong brand, competitive compensation, and a clear career ladder. The exposure to large‑scale industrial problems, mentorship from seasoned professionals, and access to state‑of‑the‑art AI labs make it an ideal launchpad for aspiring AI specialists.

Larsen & Toubro (L&T) — 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 🎯
Larsen & Toubro (L&T) Interview Preparation Corner

Comprehensive guide covering typical interview formats, common questions, and preparation tips for Larsen & Toubro (L&T) 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 (Machine Learning, Deep Learning, Python, TensorFlow, PyTorch, OpenCV, Computer Vision, Generative AI, Large Language Models, Data Preprocessing, Model Deployment, API Development, Git) & 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
Larsen & Toubro (L&T) 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 Larsen & Toubro (L&T) as a GenAI Trainee?
Preparation Tip: Highlight Larsen & Toubro (L&T)'s market reputation, recent tech innovations, and how your skills in Machine Learning 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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