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.
