Generative AI Engineering Course Overview:
This Generative AI Engineering Program is designed to train students and developers to build advanced AI-powered applications using Large Language Models (LLMs), embeddings, and modern AI architectures.
The course goes beyond basic API usage and focuses on real engineering skills like fine-tuning models, building RAG systems, working with open-source LLMs, and deploying production-ready AI applications.
Students will gain hands-on experience in designing scalable AI systems, optimizing performance, and developing real-world AI products used across industries.
Become a Generative AI Engineer
Learn to build, customize & deploy real AI systems using LLMs, RAG, and fine-tuning.
Training Duration
The program consists of 120 hours, conducted 3 days a week.
Duration: Approximately 4–5 months.
Hybrid mode available (Online + Offline), with weekend options for working professionals.
Course Fee
₹ 35,000
Course Structure
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book_2 Module 1: Python for AI Development
- • Python fundamentals for AI
- • APIs, JSON & async programming
- • FastAPI & backend basics
- • Data handling with pandas & numpy
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book_2 Module 2: LLM Foundations
- • AI vs ML vs Generative AI
- • Tokens, embeddings & transformers
- • How LLMs generate responses
- • Limitations: hallucination & bias
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book_2 Module 3: Advanced Prompt Engineering
- • Few-shot & chain-of-thought prompting
- • Structured outputs (JSON)
- • Prompt chaining & optimization
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book_2 Module 4: LLM APIs & Open Source Models
- • OpenAI & Hugging Face models
- • Model selection & parameters
- • Running local LLMs
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book_2 Module 5: Fine-Tuning & Custom Models
- • Fine-tuning concepts
- • LoRA / PEFT techniques
- • Dataset preparation & evaluation
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book_2 Module 6: Embeddings & Vector Databases
- • Semantic search & embeddings
- • FAISS & vector DBs
- • Indexing & similarity search
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book_2 Module 7: Advanced RAG Systems
- • RAG architecture (deep dive)
- • Chunking & re-ranking
- • Hybrid search techniques
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book_2 Module 8: AI Evaluation & Optimization
- • Reducing hallucinations
- • Cost & latency optimization
- • RAG vs fine-tuning decisions
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book_2 Module 9: AI System Design
- • Architecture of GenAI apps
- • API vs local models
- • Real-world system design cases
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book_2 Module 10: Deployment & MLOps
- • AI deployment strategies
- • Monitoring & logging
- • Model versioning
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book_2 Module 11: AI Security & Governance
- • Prompt injection attacks
- • Data leakage risks
- • Responsible AI practices
Capstone Projects
Students will build:
• Fine-tuned chatbot
• Advanced RAG system
• AI SaaS application
• End-to-end deployed AI system
Certification
Students will be certified as Generative AI Engineer after successful completion of the program.
Commencing Batches
Python Web Development
Jul 08, 2026
Digital Marketing
Jul 08, 2026
Angular Front-end Development
Jul 09, 2026
MERN Stack Development
Jul 09, 2026
