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 contemporary 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

- Keep course content aligned with current developments in AI and deep learning.
- Track developments in major AI frameworks, architectures and LLM technologies.
- Recommend relevant tools, datasets, libraries and learning resources.
- Periodically suggest curriculum improvements based on industry developments.
- Bring relevant real-world examples and industry use cases into the classroom.

Required Technical SkillsEssential
- Strong Python programming skills.
- Hands-on experience with PyTorch and/or TensorFlow .
- Strong understanding of deep learning fundamentals.
- Neural networks and optimization.
- Backpropagation and gradient-based learning.
- CNNs.
- Embeddings and representation learning.
- Attention mechanisms.
- Transformer architecture.
- Basic understanding of LLMs and generative AI.
- Ability to explain mathematical concepts used in deep learning.
- Ability to conduct practical coding sessions and troubleshoot learner code.

Good to Have
- NLP experience.
- Transformer-based project experience.
- LLM or Generative AI project experience.
- RAG implementation experience.
- Experience working with Hugging Face or similar ecosystems.
- Experience with model fine-tuning.
- Experience deploying AI/ML models.
- Experience working with real-world datasets.
- Research, publication or open-source contributions in AI/ML.

Experience RequiredPreferred Experience 3+ years of hands-on experience in Deep Learning / AI / Machine Learning , with substantial practical exposure to PyTorch or TensorFlow.

Candidates with experience in

- Deep Learning Engineering
- NLP
- Computer Vision
- Transformers
- Generative AI
- LLM applications
- AI/ML Research

will be particularly relevant. Strong candidates with fewer years of experience may also be considered if they demonstrate exceptional technical depth through projects, GitHub work, research, publications or other demonstrable evidence of capability .

Educational QualificationMinimum

Bachelor's degree in





- Computer Science
- Artificial Intelligence
- Data Science
- Machine Learning
- Information Technology
- Mathematics
- Statistics
- Engineering

or a closely related discipline.

Preferred

- Master's degree or PhD in Computer Science, AI, Machine Learning, Data Science or a related field.

Equivalent industry experience and a strong technical portfolio may be considered in place of advanced academic qualifications. Teaching & Communication Skills The ideal instructor should be able to:

- Explain complex technical concepts in simple language.
- Teach mathematical concepts without overwhelming learners.
- Move between theory and practical implementation effectively.
- Demonstrate code live rather than relying solely on slides.
- Encourage learners to reason through problems independently.
- Identify and address conceptual gaps.
- Mentor learners through technical projects.
- Communicate confidently with learners from varied technical backgrounds.

Ideal Candidate Profile We are looking for someone who can combine technical depth + practical AI experience + teaching ability .

The ideal candidate should be comfortable answering questions such as:

Why does backpropagation work? What is actually happening inside a neural network during training? Why do CNNs work well for image data? Why did Transformers replace many traditional sequence architectures? How does self-attention work? How are LLMs trained? What is the difference between fine-tuning, prompting and RAG?

The instructor should be able to explain these concepts clearly and then demonstrate them through code or practical examples where appropriate.

CompensationFull-Time Position

Compensation will be aligned with experience, technical depth and teaching capability.

Senior candidates with strong industry, research or specialized AI/LLM experience may be considered at a higher compensation level.

Contract / Visiting Faculty

Actual compensation may vary based on:

- Technical expertise
- Industry experience
- Teaching experience
- Course preparation requirements
- Number of sessions
- Project/capstone mentoring responsibilities
- Cohort size
- Contract duration

A fixed per-cohort engagement may also be considered. What We Expect From the Instructor The instructor will be expected to:

- Prepare and deliver structured sessions.
- Provide practical coding demonstrations.
- Develop or adapt assignments.
- Conduct learner assessments.
- Mentor mini-projects and capstone projects.
- Provide technical feedback.
- Maintain course quality and learning outcomes.
- Keep course content updated as the AI landscape evolves.

Why Join Welkin Global Academy?
- Teach a structured, industry-oriented AI program.
- Work with learners preparing for emerging technology careers.
- Opportunity to shape practical AI curriculum.
- Engage with deep learning, Transformers and modern AI concepts.
- Opportunity for full-time, visiting faculty or cohort-based engagement.
- Contribute to a growing professional education and technology-focused organization.

Application Interested candidates may share their CV, LinkedIn profile and/or GitHub/AI project portfolio , along with details of relevant teaching or industry experience.

Candidates demonstrating strong practical AI capability and the ability to teach complex concepts effectively will be given preference.

📌 AI Instructor (Tech) (Hyderabad)
🏢 Welkin Enterprises
📍 Hyderabad