Complete AI & LLM Engineering Bootcamp
From Python Fundamentals to Production-Ready AI Systems
Welcome to a comprehensive, engineering-focused bootcamp designed to take you from zero to building scalable, real-world AI applications.
This is not a surface-level course. You won’t just call APIs — you’ll architect, implement, deploy, and scale AI systems using the same core technologies behind modern LLM platforms like ChatGPT, Gemini, and Claude.
By the end of this program, you’ll have the technical depth and practical experience required to operate as an AI Engineer — not just an AI user.
What You’ll Master
1. Core Engineering Foundations
Before building AI systems, you must think like a software engineer.
You will learn:
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Python from scratch — syntax, data structures, OOP, typing, advanced patterns.
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Git & GitHub workflows — branching strategies, pull requests, collaboration best practices.
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Docker — containerization, images, volumes, networking, production-style deployment.
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Pydantic — type-safe data validation and structured output modeling for AI systems.
This foundation ensures you can build maintainable, production-ready systems.
2. AI & LLM Fundamentals
Understand what’s happening under the hood.
You’ll explore:
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How Large Language Models (LLMs) work
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Tokenization and embeddings
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Transformers and attention mechanisms
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Multi-head attention and positional encoding
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Key concepts from the Attention Is All You Need paper
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The architecture behind GPT-style systems
Concepts are explained clearly, with practical context — not abstract theory.
3. Prompt Engineering — Beyond Trial & Error
Learn systematic prompting strategies used in production systems:
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Zero-shot, one-shot, few-shot prompting
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Chain-of-thought reasoning
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Persona-based prompting
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Structured output prompting with Pydantic
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Alpaca, ChatML, and LLaMA-2 prompt formats
You’ll design prompts that produce predictable, structured, and reliable outputs.
4. Running & Integrating LLMs
Move from experimentation to real integration.
You’ll:
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Connect OpenAI and Gemini APIs using Python
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Run local models with Ollama + Docker
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Use Hugging Face and instruction-tuned models
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Expose LLMs via FastAPI endpoints
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Build modular AI services
This section bridges theory and real deployment.
5. Retrieval-Augmented Generation (RAG)
Learn how to build AI systems that use your own data.
You’ll implement:
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Complete RAG pipelines (index → retrieve → generate)
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Document loaders, splitters, retrievers, vector stores with LangChain
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Advanced RAG with Redis / Valkey queues
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Async processing patterns
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Worker-based scalable RAG systems with FastAPI
You’ll understand both the architecture and scaling considerations.
6. AI Agents & Tool-Using Systems
Go beyond chatbots.
You will:
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Build AI agents from scratch
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Create CLI-based coding agents
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Implement tool calling workflows
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Design reasoning loops
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Build autonomous task-driven systems
This is where AI systems become interactive and powerful.
7. LangGraph & Stateful AI Systems
Modern AI requires memory and state.
You’ll learn:
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Graph-based AI architecture (nodes, edges, state transitions)
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LangGraph fundamentals
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Checkpointing with MongoDB
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Short-term and long-term memory systems
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Episodic and semantic memory implementation
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Vector-based memory storage
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Graph memory using Neo4j and Cypher queries
You’ll build AI systems that remember and evolve.
8. Conversational & Multi-Modal AI
Build voice and vision-enabled AI systems.
You will:
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Integrate Speech-to-Text (STT)
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Implement Text-to-Speech (TTS)
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Build a voice-based AI coding assistant
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Work with multi-modal LLMs (image + text input)
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Design conversational pipelines
This transforms your AI apps into real interactive systems.
9. Model Context Protocol (MCP)
Understand the next layer of AI application architecture.
You’ll cover:
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What MCP is and why it matters
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MCP transport mechanisms (STDIO, SSE)
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Building an MCP server in Python
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Designing modular AI backends
This section prepares you for emerging AI system standards.
Real-World Projects You’ll Build
This bootcamp is project-driven. You will implement:
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A tokenizer from scratch
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A local AI app using Ollama + FastAPI
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A CLI-based coding assistant
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A full RAG pipeline with vector database
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A scalable queue-based RAG system
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A conversational voice agent (STT + GPT + TTS)
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A graph-memory agent with Neo4j
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An MCP-powered AI server
Each project reinforces production-grade patterns.
Who This Course Is For
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Beginners seeking a structured path into Python and AI engineering
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Developers who want to build LLM-powered applications
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Backend and data engineers integrating AI into existing systems
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Students and professionals aiming to transition into AI engineering roles
Requirements
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A computer (Windows, macOS, or Linux)
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Internet access
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No prior AI knowledge required
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Basic programming knowledge is helpful but not mandatory
The course begins with Python fundamentals and builds upward.
Why This Bootcamp Stands Out
Most AI courses stop at “call the API and print the response.”
This one goes deeper:
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System design
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Memory architecture
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Queue-based scaling
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Graph-powered reasoning
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Local model deployment
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Production-ready backend integration
You won’t just understand AI concepts — you’ll engineer AI systems.
By the End
You will be able to:
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Write production-grade Python applications
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Deploy containerized AI services
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Design RAG pipelines
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Build tool-using AI agents
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Implement memory-enabled systems
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Architect scalable AI backends
You won’t just learn AI.
You’ll build it.