GenAI Trainee at L&T – Mumbai
Company: L&T Precision Engineering & Systems
Job Role: GenAI Trainee
Location: L&T Innovation Campus, Powai, Mumbai
Experience: 0–2 years
Qualification: B.Tech / B.E.
Job Type: Trainee
Job Reference: LNT/GT/1820559
Posted On: 13 August 2026
Application End Date: 9 February 2027
Key Skills: Artificial Intelligence, Machine Learning, Generative AI, Python, Deep Learning, Computer Vision, LLMs, RAG, Data Analytics, APIs, Cloud, TensorFlow, PyTorch, OpenCV, YOLO
L&T GenAI Trainee Job Overview
L&T Precision Engineering & Systems is hiring for a GenAI Trainee position at its Innovation Campus in Powai, Mumbai. This opportunity is designed for candidates with 0–2 years of experience who have a strong interest in Artificial Intelligence, Machine Learning, Generative AI, Computer Vision, and emerging technologies.
The role offers exposure to multiple areas of modern AI development rather than focusing on only one technology. A trainee can work with machine learning models, Large Language Models, Retrieval-Augmented Generation, computer vision, data analytics, AI APIs, cloud-based solutions, and intelligent automation.
Candidates with a Bachelor of Technology (B.Tech) qualification and an interest in building practical AI solutions can consider this opportunity. The position also emphasizes continuous learning, research, experimentation, documentation, responsible AI, and collaboration with technical and business teams.
About L&T Precision Engineering & Systems
L&T Precision Engineering & Systems is part of the broader Larsen & Toubro ecosystem and operates in areas involving engineering, technology, manufacturing, and advanced solutions.
The organization focuses on applying engineering capabilities to complex industrial and technology requirements. Its innovation-oriented environment provides opportunities for professionals and trainees to work with emerging technologies and develop solutions for real-world applications.
The L&T Innovation Campus in Powai provides an environment where technology teams can work on areas such as artificial intelligence, digital technologies, engineering solutions, automation, and other emerging capabilities.
For an early-career candidate, working in such an environment can provide exposure to practical projects, technical teams, experimentation, and industry-oriented development practices.
GenAI Trainee Job Responsibilities
The GenAI Trainee role covers several technical areas. Candidates should understand that this is not limited to prompt engineering or chatbot development. The job description includes AI/ML, Generative AI, computer vision, data engineering, software development, research, documentation, and integration.
1. Machine Learning and Artificial Intelligence
The trainee will assist with collecting, cleaning, preprocessing, and validating structured and unstructured datasets.
Responsibilities may include:
- Data preprocessing
- Feature engineering
- Machine learning model development
- Deep learning model training
- Model evaluation
- Model tuning
- Performance optimization
- Model validation
- Benchmarking
- Documentation
- Model deployment and monitoring
A good understanding of the machine learning lifecycle will therefore be valuable.
The candidate may work on a project from the initial dataset preparation stage through model development, testing, deployment, and monitoring.
2. Generative AI and Large Language Models
Generative AI is one of the major areas covered by this position.
The trainee will assist in developing applications using Large Language Models (LLMs) and multimodal AI models.
The responsibilities include:
- Prompt engineering
- Prompt optimization
- Response evaluation
- Retrieval-Augmented Generation
- Fine-tuning
- Model customization
- AI chatbot development
- Virtual assistant development
- Content generation
- API-based AI integration
Candidates interested in technologies such as LLM applications, RAG systems, AI assistants, and enterprise GenAI solutions can particularly benefit from this role.
The job also emphasizes evaluating AI responses for quality, factual accuracy, safety, and compliance with Responsible AI practices. This is important because enterprise AI development requires more than simply generating responses.
3. Retrieval-Augmented Generation
RAG is specifically mentioned in the job description.
In a typical RAG application, information is retrieved from an external knowledge source and supplied to an LLM before generating a response. This approach can help organizations build AI applications that work with their own documents and enterprise information.
A candidate interested in GenAI should understand concepts such as:
- Document processing
- Text chunking
- Embeddings
- Vector search
- Retrieval
- Context generation
- LLM response generation
- RAG evaluation
Practical projects involving RAG can therefore be useful preparation for this position.
4. Computer Vision
Another important part of the role is Computer Vision Engineering.
The trainee may support solutions involving:
- Object detection
- Image classification
- Image segmentation
- Object tracking
- Optical Character Recognition
- Image preprocessing
- Video analytics
- Dataset annotation
- Data augmentation
The job specifically mentions technologies including OpenCV, TensorFlow, PyTorch, and YOLO.
Candidates who have built projects such as object detection applications, OCR systems, image classification models, or real-time video analytics applications can demonstrate relevant practical knowledge.
5. Data Analytics and Engineering
The position also involves working with large datasets and extracting useful information from them.
Responsibilities include:
- Exploratory Data Analysis
- Data visualization
- Report generation
- Dashboard creation
- Data pipeline development
- Data quality management
- Data integrity
- Data governance
The trainee may also work with different types of data, including:
- Text
- Images
- Audio
- Video
- Structured datasets
- Unstructured datasets
This makes data handling an important part of the overall skill set.
6. AI Solution Development and Integration
AI models need to be integrated into actual applications to provide business value.
The trainee will assist in integrating AI, Generative AI, and Computer Vision solutions with:
- Web applications
- Mobile applications
- Enterprise applications
- APIs
- Microservices
- Cloud platforms
The role also includes software testing, debugging, troubleshooting, version control, and following software development best practices.
Therefore, candidates who understand both AI and software development can have an advantage.
Skills Required for L&T GenAI Trainee
Artificial Intelligence
AI is the central technology area for this position. Candidates should understand basic AI concepts and how intelligent systems are developed.
Machine Learning
Knowledge of supervised and unsupervised learning, model training, evaluation, feature engineering, and model optimization is useful.
Deep Learning
Understanding neural networks and deep learning workflows will help candidates work with modern AI and computer vision applications.
Generative AI
Knowledge of LLMs, prompt engineering, RAG, fine-tuning, and AI application development is directly relevant to the role.
Large Language Models
LLMs are specifically included in the Generative AI responsibilities. Candidates should understand how LLM-based applications work and how models can be integrated into software applications.
Computer Vision
Computer vision knowledge is valuable because the role includes image and video analytics.
OpenCV
OpenCV is mentioned for computer vision development and can be used for image processing, video processing, and vision applications.
TensorFlow
TensorFlow knowledge can help with developing, training, and evaluating machine learning and deep learning models.
PyTorch
PyTorch is another important deep learning framework mentioned in the job description.
YOLO
YOLO is widely used for object detection. Practical experience with YOLO can be useful for candidates interested in real-time computer vision.
Data Analytics
EDA, visualization, reporting, and extracting insights from datasets are included in the responsibilities.
APIs and Microservices
The role involves integrating AI capabilities with applications through APIs and developing microservices.
Cloud Technologies
The job involves cloud-based AI solutions and deployment, so basic cloud knowledge can be beneficial.
Data Pipelines
Candidates should understand how data moves through preprocessing, transformation, model development, and application workflows.
Software Testing and Debugging
AI applications also require reliable software development practices. Testing and troubleshooting are explicitly mentioned.
Version Control
Knowledge of Git and general version-control practices is useful when working collaboratively on AI projects.
Responsible AI
The position specifically requires attention to safety, compliance, data privacy, cybersecurity, and ethical AI principles.
Who Can Apply?
The job requires a Bachelor of Technology (B.Tech) qualification and lists 0–2 years of experience.
The opportunity can be suitable for:
- Recent B.Tech graduates
- Early-career AI/ML professionals
- Candidates with AI/ML academic projects
- Machine Learning enthusiasts
- Generative AI learners
- Computer Vision project developers
- Candidates transitioning toward AI engineering
- Candidates with practical LLM or RAG projects
Having practical projects can be particularly valuable because the responsibilities cover multiple technologies.
What Kind of Projects Can Strengthen Your Profile?
Candidates preparing for this type of position can build projects around real-world AI applications.
Some useful examples include:
- RAG-based document assistant using an LLM and enterprise-style documents.
- AI chatbot with API integration and conversation history.
- Object detection application using YOLO.
- OCR application using OpenCV and an OCR model.
- Image classification system using TensorFlow or PyTorch.
- AI-powered web application integrating an LLM through an API.
- Data analytics project involving EDA, visualization, and machine learning.
- AI microservice exposing a trained model through an API.
These projects can help demonstrate practical understanding beyond theoretical knowledge.
Learning and Career Growth
One of the strongest aspects of this opportunity is its broad technology exposure.
A trainee working in this environment can potentially develop knowledge across several connected areas:
Data → Machine Learning → Deep Learning → Generative AI → Computer Vision → APIs → Cloud → Enterprise AI
This combination can provide a strong foundation for future positions such as:
- AI/ML Engineer
- Generative AI Engineer
- Machine Learning Engineer
- Computer Vision Engineer
- Data Scientist
- AI Software Engineer
- LLM Application Developer
- AI Solutions Engineer
The role also encourages participation in hackathons, proof-of-concept development, technical discussions, and research into emerging technologies. This is particularly useful for early-career professionals because AI technologies are evolving rapidly.
Why This GenAI Trainee Role Is Interesting for Freshers
Many entry-level AI positions focus on one specific area. This opportunity covers a much wider technology landscape.
A trainee can potentially gain exposure to traditional machine learning, deep learning, Generative AI, LLMs, RAG, computer vision, data engineering, APIs, cloud solutions, and enterprise application integration.
The position also places importance on documentation, cybersecurity, data privacy, Responsible AI, and compliance. These areas are increasingly important as organizations move AI solutions from experimentation into production.
For candidates who want to build a career in AI rather than only study AI academically, this type of role can provide valuable industry exposure.
How to Prepare for the L&T GenAI Trainee Interview
Candidates should prepare both fundamentals and practical projects.
Technical Preparation
Focus on:
- Python programming
- Machine learning fundamentals
- Deep learning fundamentals
- Common ML algorithms
- Data preprocessing
- Feature engineering
- Model evaluation
- Neural networks
- NLP basics
- LLM concepts
- Prompt engineering
- RAG architecture
- Embeddings and vector databases
- Computer vision
- OpenCV
- TensorFlow/PyTorch
- YOLO
- REST APIs
- SQL and databases
- Git
- Cloud fundamentals
Project Preparation
Be ready to explain at least one AI project from beginning to end.
You should be able to explain:
- What problem did you solve?
- What dataset did you use?
- How did you preprocess the data?
- Which model did you select?
- Why did you select it?
- How did you evaluate the model?
- What challenges did you face?
- How did you deploy it?
- What improvements would you make?
Behavioral Preparation
The company is also looking for candidates who are willing to learn and collaborate.
Prepare examples demonstrating:
- Learning a new technology
- Solving a difficult technical problem
- Working in a team
- Handling project challenges
- Participating in a hackathon or technical project
- Taking initiative
- Researching a new technology
L&T GenAI Trainee Job Details at a Glance
| Category | Details |
|---|---|
| Company | L&T Precision Engineering & Systems |
| Position | GenAI Trainee |
| Job Reference | LNT/GT/1820559 |
| Location | L&T Innovation Campus, Powai |
| Experience | 0–2 years |
| Qualification | B.Tech |
| Core Areas | AI, ML, GenAI, Computer Vision |
| Employment Level | Trainee |
| Posted On | 13 August 2026 |
| End Date | 9 February 2027 |
Final Thoughts
The L&T GenAI Trainee opening is a broad entry-level opportunity for candidates interested in Artificial Intelligence, Machine Learning, Generative AI, and Computer Vision. The job description goes beyond basic AI concepts and includes practical areas such as LLM applications, RAG, model deployment, APIs, microservices, computer vision, data pipelines, cloud solutions, and AI integration.
For fresh graduates, the most effective way to prepare is to combine strong fundamentals with practical projects. A candidate who can demonstrate Python programming, machine learning knowledge, an understanding of LLMs and RAG, and hands-on AI project experience can build a much stronger profile.
The role is particularly relevant for candidates who want to explore several areas of AI before specializing in a particular career path. With the continued growth of Generative AI, computer vision, and enterprise AI applications, exposure to these technologies can provide a useful foundation for a long-term career in AI engineering and related technology roles.
Note: The job description states that the position was posted on 13 August 2026, has an end date of 9 February 2027, and requires 0–2 years of experience with a B.Tech qualification.