L&T Precision Engineering & Systems IC has announced an opportunity for GenAI Trainee under the DEIC-L&T Precision Engineering & Systems IC team. The position is based at the L&T Innovation Campus, Powai, and is suitable for fresh graduates and candidates with up to two years of experience who want to build a career in Artificial Intelligence, Machine Learning, Generative AI, Computer Vision, and Data Engineering.
L&T GenAI Trainee Recruitment 2026 – Overview
| Particular | Details |
|---|---|
| Company | Larsen & Toubro (L&T) |
| Position | GenAI Trainee |
| Job ID | LNT/GT/1820559 |
| Department | DEIC-L&T Precision Engineering & Systems IC |
| Location | L&T Innovation Campus, Powai |
| Posted On | 13 August 2026 |
| End Date | 9 February 2027 |
| Experience | 0–2 Years |
| Qualification | Bachelor of Technology (BTech) |
| Primary Skills | Machine Learning, Artificial Intelligence |
GenAI Trainee Job Role
The GenAI Trainee role covers a broad range of modern AI technologies. Selected candidates may work with AI engineers, data scientists, software developers, and product teams to develop and integrate intelligent solutions.
1. Machine Learning & Artificial Intelligence
The trainee will assist with:
- Data collection, cleaning, preprocessing, and validation
- Feature engineering
- Machine learning and deep learning model development
- Model training and evaluation
- Hyperparameter tuning and optimization
- Model validation and benchmarking
- Performance analysis and documentation
- AI/ML model deployment and monitoring
This makes a strong foundation in Python, machine learning, statistics, data preprocessing, and model evaluation particularly useful.
2. Generative AI & LLM Development
A major component of the role is Generative AI development.
Candidates may work on:
- Large Language Model (LLM) applications
- Multimodal AI systems
- Prompt engineering
- Prompt optimization
- Response evaluation
- Retrieval-Augmented Generation (RAG)
- Fine-tuning and model customization
- AI chatbots and virtual assistants
- Content-generation applications
- LLM API and framework integration
Knowledge of technologies such as LangChain, vector databases, embeddings, Hugging Face, RAG pipelines, and LLM APIs can therefore be valuable for this type of position.
3. Computer Vision
The role also includes computer vision engineering. Responsibilities can involve:
- Image classification
- Object detection
- Image segmentation
- Object tracking
- OCR
- Image and video preprocessing
- Dataset annotation and augmentation
- Computer vision model evaluation
The job description specifically mentions technologies such as OpenCV, TensorFlow, PyTorch, and YOLO.
4. Data Analytics & Engineering
The trainee may also contribute to data-oriented tasks such as:
- Exploratory Data Analysis (EDA)
- Data visualization
- Generating analytical reports and dashboards
- Data pipeline development
- Data quality management
- Processing text, image, audio, and video datasets
- Maintaining data integrity and governance
A good understanding of Pandas, NumPy, SQL, EDA, visualization, and data preprocessing can help candidates perform these responsibilities effectively.
AI Solution Development & Integration
The position is not limited to developing models. Candidates may also be involved in integrating AI solutions into real-world applications.
This can include:
- Building AI-powered business solutions
- Integrating ML and GenAI models with web applications
- Integrating AI into mobile and enterprise applications
- Developing APIs and microservices
- Working with cloud-based AI solutions
- Software testing and debugging
- Using Git and version-control practices
This is particularly relevant for candidates who have both AI/ML and software-development skills.
Research & Innovation
L&T also expects trainees to keep up with emerging technologies in:
- Artificial Intelligence
- Machine Learning
- Deep Learning
- Generative AI
- Computer Vision
- Multimodal AI
The role may involve developing proof-of-concepts (PoCs), evaluating new AI frameworks, participating in hackathons, and researching technologies that could be adopted in future AI products.
Documentation & Responsible AI
Candidates may also be expected to document:
- Datasets
- Training processes
- Model evaluation
- Deployment procedures
- Technical implementations
- AI experiments and results
The job description also highlights Responsible AI, cybersecurity, data privacy, ethical AI, and regulatory compliance.
Therefore, understanding not only how to build an AI model but also how to develop and deploy it responsibly is important.
Eligibility
The minimum qualification mentioned for the position is:
Bachelor of Technology (BTech)
The required experience range is:
0–2 years
Therefore, the position can be relevant to fresh BTech graduates as well as candidates with some early-career experience.
The primary knowledge areas mentioned are:
- Machine Learning
- Artificial Intelligence
Skills Candidates Should Focus On
For someone preparing specifically for this type of GenAI Trainee position, the following skill stack would be useful:
Core AI/ML
- Python
- NumPy
- Pandas
- Scikit-learn
- Statistics
- Machine Learning algorithms
- Feature engineering
- Model evaluation
- Deep Learning
Generative AI
- LLM fundamentals
- Prompt engineering
- Embeddings
- Vector databases
- RAG
- LangChain
- Hugging Face
- LLM APIs
- Fine-tuning fundamentals
- Multimodal AI
Computer Vision
- OpenCV
- CNNs
- Image preprocessing
- YOLO
- Object detection
- OCR
- TensorFlow/PyTorch fundamentals
Software & Deployment
- REST APIs
- FastAPI/Django
- Git/GitHub
- Docker
- Cloud fundamentals
- Model deployment
- Monitoring
Data
- SQL
- EDA
- Data visualization
- Data pipelines
- Data quality
- Dataset management
Who Should Apply?
This position can be particularly attractive for:
- BTech graduates interested in AI/ML
- Freshers looking for GenAI opportunities
- Candidates with ML projects
- Candidates who have built RAG applications
- Candidates with LLM/API integration experience
- Computer vision project developers
- Python developers transitioning into AI
- Candidates interested in enterprise AI solutions
How to Prepare for This Role
If you are preparing for a similar GenAI Trainee interview, prioritize practical projects over only theoretical knowledge.
A strong preparation roadmap would include:
Step 1: Strengthen Python, SQL, NumPy, Pandas, and data preprocessing.
Step 2: Revise ML fundamentals including regression, classification, clustering, feature engineering, cross-validation, and evaluation metrics.
Step 3: Learn deep learning fundamentals and understand CNNs and transformer architectures.
Step 4: Build at least one complete RAG application using an LLM, embeddings, vector database, and retrieval pipeline.
Step 5: Practice prompt engineering and learn how to evaluate LLM responses for relevance, factuality, hallucination, and safety.
Step 6: Build one computer vision project using OpenCV or YOLO.
Step 7: Learn how to expose an AI model through an API using FastAPI or a similar framework.
Step 8: Understand Git, Docker, deployment, monitoring, and basic cloud concepts.
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