AI Engineering

The purpose of AI Engineering is to transform AI models into real-world applications that solve business problems, automate processes, enhance decision-making, and create innovative digital experiences.

This program is designed in multiple progressive levels starting from AI foundations, Deep Learning, Large Language Models, Generative AI, Agentic AI, Enterprise AI, Responsible AI, and AI Product Engineering. The initiate focuses on both theoretical understanding and hands-on practical implementation using industry-standard tools and frameworks.

Key skills you gain

LLM application development

Build and deploy real-world LLM applications using Python, LangChain, and LangGraph.

Prompt engineering, evaluation & APIs

Master prompt engineering and LLM evaluation while integrating multiple models — OpenAI, Anthropic Claude, Google Gemini, and Meta Llama — through OpenRouter.

RAG & vector databases

Use retrieval-augmented generation and vector databases like ChromaDB to connect models with external data.

AI agents

Design and deploy intelligent AI agents and multi-agent systems with LangGraph — using tool calling, MCP, and long-term memory to automate tasks and retain context.

Tools You Learn

Python (or JavaScript)LangChainLangGraphRetrieval-augmented generation (RAG)LLM models from various providersGoogle GeminiAnthropic Clauden8nPrompt engineeringContext engineeringVector databasesshort-term and long-term memoryStreamlitAI agentsMCPA2A

Curriculum

Transformer Architecture

Limitations of RNNs and LSTMs, Self-Attention Mechanism, Multi-Head Attention, Positional Encoding, Encoder Architecture, Decoder Architecture, Encoder-Decoder Transformers, BERT, GPT, T5, LLaMA, Mistral Architectures, Hugging Face Transformers, Fine-Tuning Transformers, Transformer-based NLP and Vision Models.

Large Language Models (LLMs)

LLM architecture, pre-training, fine-tuning, Retrieval-Augmented Generation (RAG), vector databases, hallucination handling and open-source LLM deployment. Chatbots, PDF knowledge assistant and institutional AI assistants using LangChain, Ollama, Llama Index, Chroma DB and FAISS.

Generative AI

Generative AI architectures, GPT models, prompt engineering, embeddings, tokenization, and multimodal AI systems. AI-assisted coding, content generation, AI image generation, and intelligent AI assistants using ChatGPT, Claude, Gemini, Huggingface, and Perplexity.

AI Agents

Introduction to intelligent agents, agent architectures, environments, perception, reasoning, planning, and decision-making. Development of autonomous and multi-agent systems using Large Language Models (LLMs), tool integration, memory management, and Retrieval-Augmented Generation (RAG). Design and implementation of conversational, task-oriented, and autonomous agents for real-world applications. Evaluation, deployment, monitoring, ethics, safety, and governance of AI agent systems.

Agentic AI & Autonomous Systems

Autonomous AI agents, multi-agent collaboration, planning and reasoning, memory systems, workflow automation, No-Code/Low-Code AI Agent Development using n8n, and human-in-the-loop systems. AI scheduling assistants, research agents, and enterprise automation systems using CrewAI, AutoGen, LangGraph, OpenAI Agents SDK and Microsoft Copilot Studio.