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JD #3982
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GenAI Trainee

L&T

  • Mumbai, Maharashtra
  • 4.5 LPA
  • Fresher (0-1 Yrs)
  • Posted Yesterday
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About the role

Larsen & Toubro (L&T) is one of India’s largest multi‑technology conglomerates, with a legacy spanning over eight decades. The group operates across engineering, construction, manufacturing, technology services and financial solutions, serving both domestic and global markets. L&T’s Precision Engineering & Systems division, based in Powai, focuses on cutting‑edge hardware and software solutions for aerospace, defence, and high‑end industrial applications.

The division prides itself on fostering a culture of innovation, where emerging technologies such as artificial intelligence, robotics and advanced analytics are integrated into product development.

The GenAI Trainee role is designed for fresh engineering graduates who are eager to dive into the world of generative AI, large language models (LLMs) and computer vision. As a trainee, you will work under senior AI engineers, contributing to the end‑to‑end lifecycle of AI solutions – from data collection and preprocessing to model training, evaluation and deployment. The position offers hands‑on exposure to real‑world projects that impact critical systems, making it an ideal launchpad for a career in AI research and product engineering.

Key Responsibilities

  • Assist in designing, developing and fine‑tuning machine‑learning models for vision and language tasks.
  • Participate in the creation of generative AI pipelines, including prompt engineering and model serving.
  • Conduct data cleaning, annotation and augmentation for image and text datasets.
  • Perform model testing, validation and performance benchmarking against industry standards.
  • Support integration of AI modules into larger software stacks and edge devices.
  • Contribute to documentation, version control and reproducibility of experiments.
  • Follow AI ethics guidelines, ensuring data privacy and responsible AI usage.
  • Collaborate with cross‑functional teamsβ€”hardware, firmware and product managersβ€”to translate AI capabilities into market‑ready features.
  • Prepare technical reports and presentations for internal stakeholders.
  • Stay updated with the latest research in generative AI, LLMs, and computer vision, proposing innovative ideas for the division.

Tech Stack: Python, PyTorch/TensorFlow, OpenCV, LangChain, Hugging Face Transformers, Docker, Git, AWS/GCP, CI/CD pipelines.

Growth Path: Successful trainees can progress to AI Engineer, Senior AI Engineer, and eventually AI Lead or Solution Architect roles within L&T’s technology ecosystem. The company encourages continuous learning through internal workshops, certifications and sponsorship for conferences.

Why Join L&T? L&T offers a stable, well‑structured environment backed by strong financials, exposure to large‑scale industrial projects, and a clear roadmap for career advancement. The blend of cutting‑edge AI work with real‑world engineering challenges makes it a unique place for fresh talent to grow.

Eligibility & process

Who can apply

BTech or MTech in Computer Science, Electronics, Electrical, Information Technology or related branches; minimum 60% aggregate (or CGPA 6.5/10); final year students of 2025, 2026 or 2027 batches eligible; no active backlogs at the time of joining; strong foundation in mathematics and programming.

Skills & stack

Machine Learning Artificial Intelligence Computer Vision Python TensorFlow PyTorch Data Analytics Generative AI

Selection process

  1. Round 1: Online Aptitude Test (quantitative, logical reasoning, verbal)
  2. Round 2: Technical Interview (ML concepts, coding, project discussion)
  3. Round 3: HR Interview (fitment, communication, career goals)

Prep kit

20 resources
Study material 4
Company-specific guides 4
Likely coding questions 4
Round-wise strategy 4
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, Artificial Intelligence, Computer Vision, Python, TensorFlow, PyTorch, Data Analytics, Generative AI) & 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 L&T Core Values, Learning Agility & Offer Terms Demonstrate passion, strong communication, and readiness for full-time collaboration.
Technical questions 5
What is the difference between WHERE and HAVING clauses in SQL? Answer
Model answer: WHERE filters rows before any groupings are applied, while HAVING filters aggregated groups after GROUP BY has executed.
Explain SQL Window functions: ROW_NUMBER(), RANK(), and DENSE_RANK(). Answer
Model answer: ROW_NUMBER() assigns unique sequential integers. RANK() assigns identical ranks to ties and skips ranks. DENSE_RANK() assigns identical ranks to ties without skipping rank numbers.
How do you handle NULL and missing values during data cleaning in Python/Pandas? Answer
Model answer: Use .isna().sum() to identify missing values. Impute with mean/median using .fillna() or remove with .dropna(subset=[...]) depending on variance impact.
What is the difference between Star Schema and Snowflake Schema in Data Warehousing? Answer
Model answer: Star Schema has denormalized dimension tables directly connected to the central Fact table. Snowflake Schema normalizes dimension tables into sub-dimensions to minimize redundancy.
How do you calculate MoM (Month-over-Month) growth in SQL? Answer
Model answer: Use LAG(revenue, 1) OVER (ORDER BY month) to fetch the previous month's revenue and compute (revenue - prev_revenue) / prev_revenue * 100.
Behavioural & HR questions 3
Why do you want to join L&T as a GenAI Trainee?
Highlight 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.
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?
Explain your prioritization strategy, proactive communication with mentors/peers, and agile mindset.
L&T 4.5 LPA
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