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 (Mumbai)
🏢 Larsen u0026 Toubro
📍 Mumbai