Company Overview

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.

Role Description

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.

Compensation & Engagement

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

Key Responsibilities

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

Curriculum & Technical Areas to Be Covered

1. Foundations of Generative AI

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)

2. Natural Language Processing (NLP) & Text Generation

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

3. Prompt Engineering

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

4. Fine-Tuning Large Language Models

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

5. Agentic AI & Advanced AI Workflows

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

6. LLMOps & Responsible AI

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

Technical Stack & Tools

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

Required Qualifications

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

Preferred Qualifications

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

Ideal Candidate Profile

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.

Generative AI Instructor (Contractual)