L&T GenAI Trainee Biggest hiring for freshers apply now Full Time

Job Overview

A Generative AI Trainee assists engineering teams in building, testing, and optimizing cutting-edge AI features. Responsibilities include working with Large Language Models (LLMs), writing clean Python code, testing RAG pipelines, and learning model fine-tuning. Ideal candidates have basic knowledge of machine learning concepts and AI APIs.

Key Responsibilities:

Machine Learning & Artificial Intelligence

• Assist in collecting, cleansing, preprocessing, and validating structured and unstructured datasets.
• Support the design, development, training, and evaluation of machine learning and deep learning models.
• Perform feature engineering, model tuning, and performance optimization activities.
• Conduct model validation, testing, benchmarking, and documentation.
• Assist in deploying and monitoring AI/ML models in development and production environments.
• Analyze model performance and recommend improvements for accuracy and efficiency.

Generative AI Development

• Assist in developing applications utilizing Large Language Models (LLMs) and multimodal AI models.
• Support prompt engineering, prompt optimization, and response evaluation activities.
• Participate in Retrieval-Augmented Generation (RAG), fine-tuning, and model customization projects.
• Develop and test AI-powered chatbots, virtual assistants, and content generation solutions.
• Evaluate model outputs for quality, factual accuracy, safety, and compliance with Responsible AI practices.
• Integrate Generative AI capabilities into enterprise applications through APIs and frameworks.

Computer Vision Engineering

• Assist in developing image and video analytics solutions using computer vision techniques.
• Support implementation of object detection, image classification, segmentation, tracking, and OCR models.
• Work with image and video datasets for annotation, labeling, augmentation, and preprocessing.
• Assist in training and evaluating deep learning models using frameworks such as OpenCV, TensorFlow, PyTorch, and YOLO.
• Support deployment and optimization of vision models for edge devices, cloud platforms, and real-time applications.
• Perform quality assessments and accuracy analysis of computer vision systems.

Data Analytics & Engineering

• Conduct Exploratory Data Analysis (EDA) and generate insights from large datasets.
• Create visualizations, reports, and dashboards to communicate findings effectively.
• Support development of data pipelines and workflows for AI applications.
• Ensure data quality, integrity, and governance standards are maintained.
• Assist in managing and processing image, text, audio, and video datasets.

AI Solution Development & Integration

• Collaborate in building intelligent automation and AI-driven business solutions.
• Integrate AI, Generative AI, and Computer Vision models with web, mobile, and enterprise applications.
• Assist in developing APIs, microservices, and cloud-based AI solutions.
• Participate in software testing, debugging, and troubleshooting activities.
• Follow coding standards, version control, and software development best practices.

Research & Innovation

• Stay updated on advancements in AI, Machine Learning, Generative AI, Computer Vision, and Deep Learning.
• Conduct proof-of-concept (PoC) development for emerging technologies and use cases.
• Evaluate new AI frameworks, tools, and platforms for potential adoption.
• Contribute innovative ideas for enhancing AI products, services, and operational processes.
• Participate in technical discussions, hackathons, and innovation initiatives.

Documentation & Compliance

• Prepare technical documentation, model documentation, and project reports.
• Document datasets, training procedures, model evaluations, and deployment processes.
• Follow Responsible AI, cybersecurity, data privacy, and ethical AI guidelines.
• Ensure compliance with organizational, industry, and regulatory standards.

Collaboration & Learning

• Work closely with AI Engineers, Data Scientists, Software Developers, Product Teams, and Business Stakeholders.
• Participate in Agile ceremonies, code reviews, and team meetings.
• Continuously enhance technical knowledge through training, mentoring, and self-learning.
• Demonstrate a proactive attitude toward learning and adopting emerging technologies.

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