IT Trainer

Larsen & Toubro

Chennai District

On-site

INR 900,000 - 1,500,000

Full time

20 hours ago
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Job summary

Larsen & Toubro in Chennai district seeks an AI/ML curriculum designer to craft industry-aligned learning paths and hands-on materials. You will lead instructor-led sessions, mentor learners, and build rigorous assessments.

You will collaborate with instructional designers and partners, integrating GenAI concepts and modern ML tools while keeping content up-to-date with industry trends.

Qualifications

  • Proficiency in AI/ML curriculum design and delivery.
  • Strong programming and problem-solving fundamentals.
  • Experience creating project-based learning content.
  • Hands-on industry experience as a Data Scientist or ML Engineer is a plus.

Responsibilities

  • Design and develop industry-aligned AI/ML curriculum and learning paths.
  • Create engaging training materials: presentations, labs, exercises, case studies.
  • Deliver instructor-led classroom and virtual training sessions.
  • Mentor students during hands-on labs, capstone projects and hackathons.
  • Develop practical assessments and evaluation rubrics.
  • Review and enhance content based on industry trends.
  • Collaborate with instructional designers and academic partners.
  • Provide technical guidance to trainers and enable faculty programs.
  • Stay updated with emerging AI/ML technologies and integrate them into the curriculum.

Skills

Python
Data Structures
ML Algorithms
TensorFlow PyTorch
SQL
Data Visualization

Education

Bachelors in CS/IT/AI/DS
Masters in CS/IT/AI/DS
BE/BTech / MCA encouraged

Tools

Git & GitHub
REST APIs
Cloud Platforms
MLOps
Jupyter
Streamlit
Docker
Flask
FastAPI

Job description

  • Design and develop industry-aligned curriculum and learning paths in AI/ML.
  • Create engaging training materials, including presentations, lab manuals, coding exercises, case studies, assignments, and projects.
  • Deliver instructor-led classroom and virtual training sessions.
  • Mentor students during hands-on labs, capstone projects, and hackathons.
  • Develop practical assessments, coding challenges, and evaluation rubrics.
  • Review and continuously enhance existing learning content based on industry trends.
  • Collaborate with instructional designers and academic partners to improve learner outcomes.
  • Provide technical guidance to trainers and support faculty enablement programs.
  • Stay updated with emerging AI/ML technologies and integrate them into the curriculum.
Key Responsibilities
  • Design and develop industry-aligned curriculum and learning paths in AI/ML.
  • Create engaging training materials, including presentations, lab manuals, coding exercises, case studies, assignments, and projects.
  • Deliver instructor-led classroom and virtual training sessions.
  • Mentor students during hands-on labs, capstone projects, and hackathons.
  • Develop practical assessments, coding challenges, and evaluation rubrics.
  • Review and continuously enhance existing learning content based on industry trends.
  • Collaborate with instructional designers and academic partners to improve learner outcomes.
  • Provide technical guidance to trainers and support faculty enablement programs.
  • Stay updated with emerging AI/ML technologies and integrate them into the curriculum.
Required Technical Skills
Mathematics & Statistics for Machine Learning
  • Statistics and Probability
  • Linear Algebra
  • Basic Calculus (Differentiation, Optimization)
Programming & Problem Solving
  • Core Python Programming Concepts
  • Data Structures and Algorithmic Problem Solving
  • Pandas and NumPy for Data Manipulation
  • Data Preprocessing and Feature Engineering
Machine Learning
  • Supervised and Unsupervised Learning Algorithms
  • Model Evaluation Metrics (Accuracy, Precision/Recall, F1, ROC-AUC, RMSE, etc.)
  • Scikit-learn
  • Bias-Variance Tradeoff, Overfitting/Underfitting, Regularization
Deep Learning
  • Deep Learning Fundamentals
  • CNNs, RNNs/LSTMs, Transfer Learning Models, VAE, and GAN
  • Computer Vision and NLP concepts
  • TensorFlow or PyTorch (at least one)
Data Analytics & Visualization
  • Advanced Excel for Data Analysis
  • Power BI Fundamentals, Tableau, Dashboard Development, and Data Storytelling
Database
  • SQL Fundamentals and Database Querying (Joins, Aggregations, Subqueries)
Generative AI & Agentic AI
  • Basics of Generative AI (GenAI)
  • LLM – Transformer Architecture and Working Principles
  • RAG, Prompt Engineering and Fine-tuning techniques
  • Agentic AI Concepts and Multi-Agent Systems
Preferred Skills
  • Git and GitHub
  • REST APIs (Flask or Fast API)
  • Cloud platforms (Azure, AWS, or GCP) - especially AI/ML services
  • MLOps Basics (model deployment, versioning, monitoring)
  • Jupyter Notebook
  • Streamlit or Gradio
  • Docker
Educational Qualification
  • Bachelor's or Master's degree in Computer Science, Information Technology, Artificial Intelligence, Data Science, or a related field.
  • Candidates with a Bachelor of Engineering (B.E./B.Tech.), Master of Engineering (M.E./M.Tech.), MCA, or equivalent qualifications are encouraged to apply.
Required Competencies
  • Excellent communication and presentation skills.
  • Strong analytical and problem-solving abilities.
  • Passion for teaching and mentoring.
  • Ability to simplify complex mathematical and technical concepts.
  • Strong curriculum development and documentation skills.
  • Effective stakeholder management and collaboration.
  • Time management and multitasking abilities.
Preferred Experience
  • Experience in EdTech, higher education, or corporate training.
  • Experience delivering technical training to engineering students or working professionals.
  • Experience creating project-based learning content and coding assessments.
  • Hands‑on industry experience as a Data Scientist, ML Engineer, or AI Engineer.
  • Experience with Learning Management Systems (LMS) is an added advantage.
Key Deliverables
  • Curriculum and syllabus design for Lab programs
  • Instructor presentations
  • Lab manuals and coding exercises
  • Assignments and assessments
  • Capstone projects
  • Question banks and evaluation rubrics
  • Faculty enablement sessions
  • Student mentoring and technical support
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