GEN AI Instructor

Generative AI Instructor

We are seeking an experienced and passionate

Generative AI Instructor

to educate and empower aspiring developers, data scientists, and business professionals in the rapidly evolving field of Generative AI (GenAI). The ideal candidate will have 1-3 years of technical experience in

Machine Learning (ML) , with significant

hands-on experience

in developing, training, and deploying generative models.

This role is critical in bridging the gap between foundational AI / ML knowledge and practical, real-world Generative AI application development.

Roles & Responsibilities

Conduct Structured Training :

Design and lead comprehensive training programs on the theory, implementation, and application of Generative AI technologies, including

Large Language Models (LLMs) ,

Generative Adversarial Networks (GANs) , and

Diffusion Models .

Curriculum Development :

Create and update high-quality learning materials, hands-on labs, guided projects, assignments, and assessments to ensure a cutting-edge and effective learning experience.

Core Concept Instruction :

Teach fundamental concepts such as

deep learning architectures

(Transformers, VAEs),

prompt engineering ,

model fine-tuning (e.g., LoRA) ,

transfer learning , and

ethical AI

principles.

Project Mentorship :

Guide learners in building practical GenAI applications across various modalities, such as

text generation (chatbots, summarization) ,

image / video synthesis , and

code generation .

Technical Support & Review :

Review learner code, troubleshoot complex technical implementations, and provide one-on-one technical mentorship.

Stay Current :

Maintain deep expertise in the latest GenAI research, open-source models, frameworks, and cloud services.

Facilitate Interactive Learning :

Host live coding sessions, practical workshops, and in-depth Q&A / debugging sessions.

Adapt Pedagogy :

Adjust teaching methodology and content based on the learners' technical background, ranging from foundational Python / ML knowledge to advanced Generative AI development.

Technology-Specific Responsibilities

Generative Model Theory & Implementation :

Teach the underlying principles of

LLMs, GANs, and VAEs / Diffusion Models .

Guide implementation of generative models using frameworks like

PyTorch

or

TensorFlow / Keras .

Large Language Models (LLMs) & APIs :

Instruct on working with

commercial APIs

(OpenAI, Gemini, Anthropic) and

open-source models

(Hugging Face ecosystem).

Cover techniques for

prompt optimization, system-level instructions, function calling / tool use , and

managing token usage / cost .

Train on advanced techniques like

Retrieval-Augmented Generation (RAG)

and

parameter-efficient fine-tuning (PEFT / LoRA) .

Multimodal Generative AI :

Provide training on image generation, manipulation, and video synthesis using models like

Stable Diffusion

or equivalent, including concepts like

inpainting

and

ControlNet .

Explore code generation and pair programming use cases with GenAI tools.

Deployment & MLOps :

Instruct on model serialization, versioning, and deployment strategies for generative models using platforms like

Hugging Face Hub ,

Docker , and cloud AI services.

Emphasize

cost-efficient scaling

and

real-time inference .

Responsible AI & Ethics :

Dedicate modules to the ethical implications of GenAI, focusing on

bias detection / mitigation ,

content moderation ,

safety alignment , and

data privacy / copyright

considerations.

Requirements

1-3 years of professional experience

in Python programming and Machine Learning / Deep Learning.

Demonstrable practical experience

in building, training, and deploying Generative AI models (LLMs, GANs, VAEs, or Diffusion Models).

Expertise in at least one major deep learning framework ( PyTorch

or

TensorFlow / Keras ).

Strong understanding of

LLM architectures (e.g., Transformer) ,

embeddings , and

vector databases

for RAG.

Exceptional written and verbal communication skills, with a proven ability to mentor and explain complex technical concepts to diverse audiences.

Preferred Skills

Experience with advanced LLM frameworks like

LangChain, LlamaIndex, or Haystack .

Familiarity with cloud-based AI / ML platforms (AWS SageMaker, Google Vertex AI, Azure ML).

Prior experience in a technical training, teaching, or mentorship role.

Experience with

MLOps tools

for generative models (e.g., weights & biases, MLflow).

Working knowledge of deploying AI applications using

FastAPI, Streamlit, or Gradio .