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GenAI Trainee

Larsen & Toubro (L&T)

  • Mumbai, India
  • 3.5 LPA
  • 2 Years
  • Posted Today
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About the role

Larsen & Toubro Technology Services (LTTS) is a leading Indian multinational engineering and technology services company headquartered in Mumbai. With a legacy spanning over six decades, LTTS delivers endโ€‘toโ€‘end solutions across sectors such as aerospace, automotive, telecom, healthcare, and energy. The organization is known for its strong focus on innovation, research and development, and a culture that encourages continuous learning.

As part of the larger L&T Group, LTTS benefits from deep industry expertise, a robust global delivery network, and a commitment to sustainable growth.

The GenAI Trainee role is designed for fresh engineering graduates who are eager to dive into the fastโ€‘evolving world of Generative AI. Trainees will work under senior AI Engineers and Data Scientists, gaining handsโ€‘on experience in building, fineโ€‘tuning, and deploying large language models (LLMs) and multimodal AI solutions. The position offers a blend of data engineering, model development, and product integration tasks, providing a solid foundation for a career in artificial intelligence.

Key Responsibilities

  • Collect, cleanse, preprocess, and validate structured and unstructured datasets for AI projects.
  • Assist in designing, developing, training, and evaluating machine learning and deep learning models.
  • Perform feature engineering, model tuning, and performance optimization.
  • Conduct model validation, benchmarking, and documentation of results.
  • Support deployment and monitoring of AI/ML models in development and production environments.
  • Develop applications leveraging Large Language Models, including prompt engineering and response evaluation.
  • Participate in Retrievalโ€‘Augmented Generation (RAG), fineโ€‘tuning, and model customization initiatives.
  • Build and test AIโ€‘powered chatbots, virtual assistants, and contentโ€‘generation tools.
  • Ensure data quality, integrity, and governance across image, text, audio, and video datasets.
  • Collaborate with crossโ€‘functional teamsโ€”AI Engineers, Software Developers, Product Managers, and Business Stakeholdersโ€”to integrate AI capabilities into enterprise solutions.

Tech Stack: Python, PyTorch/TensorFlow, Hugging Face Transformers, SQL, Pandas, NumPy, Docker, RESTful APIs, Git, Linux.

Growth Path: Successful trainees can progress to AI Engineer, Machine Learning Engineer, or Data Scientist roles within 12โ€‘24 months, with opportunities to lead AI projects, specialize in LLM research, or move into product management.

Why Join LTTS? The company offers a vibrant learning ecosystem, mentorship from industry veterans, exposure to cuttingโ€‘edge AI research, and a clear career trajectory. Freshers benefit from structured training programs, access to stateโ€‘ofโ€‘theโ€‘art infrastructure, and the chance to work on realโ€‘world AI solutions for global clients, making LTTS an ideal launchpad for a thriving AI career.

Eligibility & process

Who can apply

Bachelor's degree in Computer Science, Information Technology, Electronics & Communication, Electrical Engineering, or related streams; minimum 60% aggregate (or CGPA 6.0/10); no active backlogs at the time of joining; fresh graduates or candidates with up to 2 years of experience; strong academic record and a passion for AI/ML; willingness to relocate to Mumbai.

Skills & stack

Python Machine Learning Deep Learning Data Preprocessing SQL Git Linux NLP

Selection process

  1. Round 1: Online Assessment (quantitative aptitude, logical reasoning, verbal ability, basic programming, AI/ML fundamentals)
  2. Round 2: Technical Interview (project discussion, coding, AI concepts, problemโ€‘solving)
  3. Round 3: Managerial/Technical Discussion (scenarioโ€‘based questions, AI application in business)
  4. Round 4: HR Interview (communication skills, cultural fit, career aspirations)

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 (Python, Machine Learning, Deep Learning, Data Preprocessing, SQL, Git, Linux, NLP) & 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.
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 Larsen & Toubro (L&T) as a GenAI Trainee?
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.
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.
Larsen & Toubro (L&T) 3.5 LPA
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