IT Trainer

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