GEN AI Instructor

Generative AI Instructor

Generative AI Instructor

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

  .