Generative AI Instructor (Contractual)

The Delhi School of Skill Development (DSSD), established in 2015 and widely recognized as The AI Gurukul, is committed to transforming skill development for the modern digital economy. With a strong focus on innovation-driven education, DSSD specializes in high-demand domains including Artificial Intelligence, Data Analytics, Data Science, Automation, and Digital Technologies.

DSSD emphasizes practical, industry-oriented learning through live projects, hands-on AI workflows, real-world implementations, and application-based training methodologies. The institution is dedicated to preparing future-ready professionals equipped with the technical, analytical, and problem-solving skills required in today's rapidly evolving AI ecosystem.

DSSD is seeking a highly skilled and passionate Generative AI Trainer to deliver advanced classroom training programs focused on modern AI technologies, Large Language Models (LLMs), Prompt Engineering, AI Agents, and real-world AI application development.

The trainer will be responsible for conducting engaging, practical, and industry-oriented sessions through structured learning modules, hands-on workshops, coding demonstrations, implementation exercises, and project-based learning methodologies.

The ideal candidate should be capable of independently handling the complete training lifecycle, including curriculum delivery, workshop execution, learner guidance, technical mentoring, assignment evaluation, and practical AI demonstrations.

This is a contractual, on-site role based in Delhi, India.

Contractual engagement for 1 to 2 months

Monthly compensation ranging between 35,000 - 45,000

Final compensation will depend on technical expertise, practical experience, and training delivery capabilities

• Deliver comprehensive training sessions on Generative AI and AI-powered workflows
• Conduct practical workshops, coding demonstrations, and real-world AI implementation sessions
• Explain complex AI concepts in a structured and student-friendly manner
• Guide learners through hands-on projects, assignments, and practical exercises
• Mentor students on AI application development, automation systems, and LLM-based workflows
• Create an engaging, interactive, and industry-focused learning environment
• Evaluate learner performance and provide constructive technical feedback
• Stay updated with the latest advancements in Generative AI, LLMs, Prompt Engineering, and Agentic AI systems
• Ensure effective completion of the assigned curriculum within the training timeline

a. Introduction to Generative AI

b. History and evolution of AI systems

c. Predictive AI vs. Generative AI

d. Deep Learning fundamentals

e. Neural Networks

f. Feedforward Networks

g. Backpropagation

h. Transformers and Attention Mechanisms

i. Attention Is All You Need architecture

j. Variational Autoencoders (VAEs)

k. Generative Adversarial Networks (GANs)

a. NLP fundamentals

b. Text preprocessing and cleaning

c. Tokenization techniques

d. Embeddings and vector representations

e. Large Language Models (LLMs)

f. GPT-3 / GPT-4

g. Gemini

h. LLaMA

i. Claude

j. OpenAI APIs

k. Hugging Face ecosystem

l. Model access and practical text generation exercises

a. Fundamentals of prompting

b. Prompt structuring for accurate outputs

c. Zero-shot prompting

d. Few-shot prompting

e. Chain-of-thought prompting

f. Persona prompting

g. Prompt optimization techniques

h. Prompt chaining and workflow pipelines

i. Breaking complex tasks into modular prompt systems

a. Fine-tuning vs. Retrieval-Augmented Generation (RAG)

b. Model customization strategies

c. Parameter-Efficient Fine-Tuning (PEFT)

d. LoRA and QLoRA techniques

e. Instruction fine-tuning

f. Training models for domain-specific tasks

a. Introduction to AI agents

b. Autonomous reasoning and execution systems

c. Multi-agent workflows

d. AI orchestration concepts

e. Frameworks such as:

• AutoGen
• LangGraph
• CrewAI

f. Tool usage and function calling

g. Integrating AI agents with APIs, databases, search engines, and external systems

a. LLM evaluation metrics:

• ROUGE
• BLEU
• RLHF concepts

b. Human feedback loops

c. Bias detection and ethical AI considerations

d. Hallucination management techniques

e. Prompt injection awareness and AI security

f. AI application deployment using:

• FastAPI
• Docker
• Streamlit

Candidates should have practical exposure to tools and platforms such as:

• Python
• OpenAI API
• Hugging Face
• TensorFlow / PyTorch
• LangChain
• LangGraph
• CrewAI
• AutoGen
• FastAPI
• Streamlit
• Docker
• Vector Databases
• RAG Pipelines
• LLM Deployment Workflows

• Strong expertise in Artificial Intelligence, Machine Learning, and Generative AI
• Deep understanding of LLMs, Prompt Engineering, NLP, and AI workflows
• Experience working with real-world datasets and AI implementation projects
• Practical knowledge of AI tools, APIs, and modern AI frameworks
• Proficiency in Python programming
• Familiarity with TensorFlow, PyTorch, or similar frameworks
• Ability to conduct technical workshops and hands-on learning sessions
• Strong analytical thinking and problem-solving skills
• Excellent communication and presentation abilities
• Capability to simplify technical concepts for learners from diverse backgrounds

• Prior teaching, mentoring, or technical training experience in AI-related domains
• Experience conducting bootcamps, workshops, or academic sessions
• Background in Computer Science, Artificial Intelligence, Data Science, or related fields
• Experience building AI-powered applications or automation workflows
• Passion for education, innovation, and workforce upskilling in the AI era

The ideal candidate combines strong technical expertise with excellent teaching ability and can confidently deliver advanced Generative AI concepts through practical, application-oriented learning approaches. The trainer should be capable of bridging the gap between theoretical AI concepts and real-world implementation while creating an engaging and future-focused learning environment for students and learners.