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 Concep

📌 It Trainer Mumbai
🏢 LT-Larsen & Toubro
📍 Mumbai