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.
• Robust 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

📌 IT Trainer (Chennai)
🏢 Larsen and Toubro
📍 Chennai