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 .