GenAI Trainee
L&T
About the role
Larsen & Toubro (L&T) is one of Indiaβs largest multiβtechnology 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&Tβs Precision Engineering & Systems division, based in Powai, focuses on cuttingβedge hardware and software solutions for aerospace, defence, and highβend 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 endβtoβend lifecycle of AI solutions β from data collection and preprocessing to model training, evaluation and deployment. The position offers handsβon exposure to realβworld 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 fineβtuning machineβlearning 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 crossβfunctional teamsβhardware, firmware and product managersβto translate AI capabilities into marketβready 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&Tβs technology ecosystem. The company encourages continuous learning through internal workshops, certifications and sponsorship for conferences.
Why Join L&T? L&T offers a stable, wellβstructured environment backed by strong financials, exposure to largeβscale industrial projects, and a clear roadmap for career advancement. The blend of cuttingβedge AI work with realβworld 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
Selection process
- Round 1: Online Aptitude Test (quantitative, logical reasoning, verbal)
- Round 2: Technical Interview (ML concepts, coding, project discussion)
- Round 3: HR Interview (fitment, communication, career goals)
Prep kit
20 resourcesStudy material 4
Company-specific guides 4
Likely coding questions 4
Round-wise strategy 4
Technical questions 5
What is the difference between WHERE and HAVING clauses in SQL? Answer
Explain SQL Window functions: ROW_NUMBER(), RANK(), and DENSE_RANK(). Answer
How do you handle NULL and missing values during data cleaning in Python/Pandas? Answer
What is the difference between Star Schema and Snowflake Schema in Data Warehousing? Answer
How do you calculate MoM (Month-over-Month) growth in SQL? Answer
Behavioural & HR questions 3
Highlight L&T's market reputation, recent tech innovations, and how your skills in Machine Learning directly solve their team's objectives.
Use the STAR method: Situation (project context), Task (what needed solving), Action (specific tools/logic applied), Result (quantifiable positive outcome).
Explain your prioritization strategy, proactive communication with mentors/peers, and agile mindset.