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REQUIREMENT_ID_1776 β€’ 3-DAY_ACTIVE_POLICY

GenAI Trainee

Company
Company Larsen & Toubro (L&T)
Type
Opportunity Type Internship
Salary
Stipend / Salary 4.5 - 9 LPA
Location
Location Mumbai, Maharashtra
Posted Date
Posted Date Today
Python Machine Learning Deep Learning Generative AI Large Language Models Prompt Engineering Retrieval Augmented Generation Computer Vision OpenCV TensorFlow PyTorch YOLO Data Preprocessing EDA REST APIs Git Docker Cloud AI Services
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Aptitude Practice Questions Open Resource β†—
Helps you prepare for the online aptitude test commonly used by L&T for screening freshers.
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Technical Interview Preparation Guide Open Resource β†—
Covers core AI/ML concepts and coding problems frequently asked in L&T technical interviews.
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Comprehensive Interview Resources Open Resource β†—
Offers curated notes, mock interviews, and interview experiences relevant to L&T recruitment.
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Coding Practice Platform Open Resource β†—
Provides a large set of algorithmic problems to sharpen coding skills needed for L&T technical rounds.

β€’ Bachelor of Technology (B.Tech) or Master of Technology (M.Tech) in Computer Science, Electronics, Electrical, Information Technology or related engineering streams. β€’ Minimum aggregate 60% (or CGPA equivalent) in the qualifying degree. β€’ Fresh graduates or candidates with up to 2 years of relevant experience. β€’ No active backlogs at the time of application. β€’ Graduation batch years 2024, 2025, 2026 or 2027 are acceptable. β€’ Strong interest in AI/ML, Generative AI, and Computer Vision. β€’ Good communication skills in English.

1
Round 1: Online Aptitude Test (Logical Reasoning, Quantitative Aptitude, Verbal)
2
Round 2: Technical Interview (AI/ML concepts, coding, project discussion)
3
Round 3: HR Interview (fitment, motivation, communication)
Larsen & Toubro (L&T) is one of India’s largest engineering conglomerates, operating across infrastructure, construction, manufacturing, technology and digital services. With a legacy spanning over eight decades, L&T has built a reputation for delivering complex, high‑value projects worldwide while fostering a culture of innovation and continuous learning. The company’s Precision Engineering & Systems division focuses on cutting‑edge technologies, and its Innovation Campus in Powai, Mumbai, serves as a hub for research, product development, and emerging‑tech initiatives. The GenAI Trainee role is designed for fresh engineering graduates (B.Tech/M.Tech) who are passionate about Artificial Intelligence, Machine Learning, Generative AI, and Computer Vision. As a trainee, you will work alongside senior AI engineers and data scientists, gaining hands‑on experience in the entire AI lifecycleβ€”from data collection and preprocessing to model development, evaluation, and deployment. The position offers exposure to large language models (LLMs), Retrieval‑Augmented Generation (RAG), prompt engineering, and cloud‑based AI services, making it an ideal launchpad for a career in the rapidly evolving AI domain. **Key Responsibilities** 1. Collect, cleanse, and preprocess structured and unstructured datasets for AI projects. 2. Assist in designing, training, and fine‑tuning machine‑learning and deep‑learning models. 3. Perform feature engineering, model tuning, and performance optimization. 4. Develop applications using Large Language Models, including prompt engineering and RAG solutions. 5. Build and test AI‑powered chatbots, virtual assistants, and content‑generation tools. 6. Create computer‑vision pipelines for image/video analytics, object detection, segmentation, and OCR. 7. Conduct exploratory data analysis, visualizations, and dashboard creation. 8. Support integration of AI models into web, mobile, and enterprise applications via APIs and micro‑services. 9. Document model training, evaluation procedures, and maintain responsible‑AI compliance. 10. Stay updated with the latest research in AI, propose PoCs, and contribute ideas in hackathons and innovation forums. **Tech Stack**: Python, TensorFlow, PyTorch, OpenCV, YOLO, Hugging Face Transformers, LangChain, REST APIs, Git, Docker, AWS/GCP AI services. **Growth Path**: Successful trainees can progress to roles such as AI Engineer, Machine Learning Engineer, Generative AI Engineer, Computer Vision Engineer, or Data Scientist within L&T’s technology ecosystem. The company encourages internal mobility, continuous upskilling, and mentorship programs. **Why Join L&T?** L&T offers a stable, globally recognized brand, access to large‑scale real‑world projects, and a collaborative environment that values innovation. Freshers get the chance to work on state‑of‑the‑art AI solutions while receiving structured training, mentorship, and clear career progression pathways.

Larsen & Toubro (L&T) β€” AI & Machine Learning Interview Guide

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

APTITUDE & LOGIC πŸ“–
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 β†—
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 β†—
MOCK TESTS & PAPERS πŸ“
Comprehensive Interview Prep Resources & Syllabus

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

Open Resource β†—
CODING PRACTICE πŸ’»
Algorithm and Data Structure Problem Set

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

Open Resource β†—
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, Generative AI, Large Language Models, Prompt Engineering, Retrieval Augmented Generation, Computer Vision, OpenCV, TensorFlow, PyTorch, YOLO, Data Preprocessing, EDA, REST APIs, Git, Docker, Cloud AI Services) & 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 β–Ό
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 β–Ό
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 β–Ό
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 β–Ό
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 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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