AI Instructor (Tech)

Position : AI Instructor

Location: Hyderabad / Hybrid / Online

Employment Type: Full-Time / Contract / Visiting Faculty

Course: AI Foundation — Deep Learning & Modern AI

About Welkin Global Academy

Welkin Global Academy, a unit of Welkin Enterprises LLP, focuses on industry-oriented education and professional development designed to bridge the gap between academic learning and workplace expectations.

Role Overview

We are looking for an AI Instructor who can make technically complex AI concepts understandable while ensuring that learners gain meaningful hands-on experience.

The AI Instructor will be responsible for delivering Welkin Global Academy’s Program. , covering the fundamentals of deep learning through to modern AI and LLM concepts.

The ideal candidate should be able to move comfortably between mathematical foundations, conceptual explanations, Python/PyTorch implementation, practical projects, and current developments in AI .

This is not a framework-only teaching role. The instructor should be capable of explaining how and why neural networks work , including concepts such as backpropagation, optimization, attention, transformers, and model training.

Key Responsibilities1. Deep Learning Foundations

• Teach neural network fundamentals from first principles.
• Explain perceptrons, activation functions, forward propagation and computational graphs.
• Teach loss functions, optimization and gradient descent.
• Explain backpropagation conceptually and mathematically , including derivation where appropriate.
• Help learners understand how neural networks learn rather than simply how to use a framework.
• Connect mathematical concepts to practical implementation.

1. PyTorch & Hands-on Development

• Teach practical implementation using Python and PyTorch .
• Cover tensors, datasets, data loaders and model architecture.
• Demonstrate PyTorch’s autograd and computational graph concepts.
• Guide learners in building and training feedforward neural networks.
• Introduce model evaluation, validation, overfitting and regularization.
• Develop practical exercises and coding assignments.
• Debug learner implementations and explain common training issues.

1. Convolutional Neural Networks

• Introduce CNN architecture and the principles behind convolution.
• Explain filters, feature maps, pooling and hierarchical feature extraction.
• Build and train CNN models using PyTorch.
• Demonstrate practical computer vision applications.
• Help learners understand model performance and common challenges in image-based learning.

1. Sequence Models & Representation Learning

• Introduce sequence modelling and its applications.
• Explain embeddings and representation learning.
• Cover the evolution from traditional sequence models toward attention-based architectures.
• Introduce attention mechanisms conceptually and through practical examples.

1. Transformers & Modern AI

• Explain the architecture and working principles of Transformers.
• Cover self-attention, multi-head attention, positional encoding and encoder/decoder concepts.
• Demonstrate why Transformers became important in modern AI.
• Connect Transformer architecture to modern NLP and generative AI systems.
• Help learners understand the relationship between classical deep learning and modern AI architectures.

1. LLM Fundamentals

Introduce learners to the foundations of Large Language Models, including:

• How language models learn representations.
• Tokenization and embeddings.
• Pre-training and fine-tuning.
• Inference and generation.
• Prompting fundamentals.
• Context windows and model behaviour.
• Embeddings and semantic search.
• Retrieval-Augmented Generation (RAG) at a conceptual and introductory practical level.
• Overview of the current LLM ecosystem and applications.

The instructor is not expected to turn the course into an advanced LLM engineering program, but should be able to explain the underlying concepts accurately and demonstrate appropriate hands-on examples.

1. Projects & Assessment

• Design and deliver practical coding exercises.
• Develop and evaluate a Deep Learning mini-project .
• Guide learners through an AI Foundation capstone project .
• Establish project evaluation criteria and rubrics.
• Provide constructive technical feedback.
• Assess learners based on conceptual understanding, implementation quality and problem-solving ability.

1. Curriculum & Industry Relevance

-…