IT Trainer

Job description

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

Qualifications

Master of Technology (MTech)