Complete AI & LLM Engineering
Bootcamp Program
From Zero to Production-Ready AI Engineer.
Learn Python, Git, Docker, Pydantic, LLMs, agents, RAG, LangChain, LangGraph, voice AI, and MCP in one place.
Every lesson is hands-on and built around real-world production workflows. Build, deploy, and scale the same kinds of AI systems powering ChatGPT, Gemini, and Claude.
Total Course Fee
45 days • Monday to Friday • Live online
What is Included
Limited seats for this cohort
What This Course Is
This is not a theory course. Every concept is applied in code so you build real systems while learning.
Hands-On First
Build agents, RAG, and production APIs from day one.
Production-Ready ✓
Deploy and scale AI apps like real-world engineering teams.
Bootcamp Outcome
Build, deploy, and scale AI systems that power assistants, agents, and retrieval workflows.
Why This Course Stands Out
Go beyond API calls with real system design, memory, and scaling.
Complete Curriculum
Eight focused parts that take you from fundamentals to production-grade AI systems.
Part 1 — Foundations
Build the engineering base every AI developer needs.
- Python from scratch — syntax, data types, OOP, and advanced features
- Git & GitHub — branching, merging, collaboration, and professional workflows
- Docker — containerization, images, volumes, and deploying applications
- Pydantic — type-safe, structured data handling for modern Python apps
Part 2 — AI & LLM Fundamentals
Understand how LLMs work under the hood.
- What are LLMs and how GPT predicts the next token
- Tokenization, embeddings, and the attention mechanism explained simply
- Multi-head attention, positional encodings, and the Attention Is All You Need paper
- Base models vs instruction-tuned models and when to use each
Part 3 — Prompt Engineering
The craft of communicating with AI models.
- Core strategies: zero-shot, one-shot, few-shot, chain-of-thought, persona-based
- Prompt formats: Alpaca, ChatML, LLaMA-2
- Designing prompts for structured JSON outputs with Pydantic
- Prompt anti-patterns that cause hallucinations and how to fix them
Part 4 — Running & Using LLMs
Connect to cloud APIs and run models locally.
- OpenAI & Gemini API setup and integration with Python
- Running models locally with Ollama + Docker with zero API cost
- Using Hugging Face models and INSTRUCT-tuned variants
- Connecting LLMs to FastAPI endpoints for production use
Part 5 — Agents & RAG Systems
Build AI that retrieves knowledge and takes action.
- Build your first AI agent from scratch with the ReAct loop
- CLI-based coding agents with Claude
- The complete RAG pipeline: indexing → retrieval → answering
- LangChain: document loaders, splitters, retrievers, and vector stores
- Advanced RAG with Redis or Valkey queues for async processing
- Scaling RAG with background workers and FastAPI
Part 6 — LangGraph & Memory
Give your agents state, persistence, and long-term memory.
- LangGraph fundamentals: state, nodes, edges, and graph-based AI workflows
- Checkpointing with MongoDB to resume agents across sessions
- Memory architecture: short-term, long-term, episodic, and semantic memory
- Implementing memory layers with Mem0 and vector databases
- Graph memory with Neo4j and Cypher queries
Part 7 — Conversational & Multi-Modal AI
Build agents that see, speak, and listen.
- Voice-based conversational agents with real-time interaction
- Integrating Speech-to-Text and Text-to-Speech
- Build your own AI voice assistant for coding with a Cursor IDE style workflow
- Multi-modal LLMs that process images and text together in one pipeline
Part 8 — Model Context Protocol (MCP)
The open standard that connects AI to everything.
- What MCP is and why it is becoming the standard for AI integrations
- MCP transports: STDIO and SSE
- Build and deploy an MCP server with Python
Projects You Will Build
Eight portfolio-grade builds that prove production skills.
Tokenizer from scratch
Implement tokenization rules and build a custom tokenizer pipeline.
Local Ollama + FastAPI AI app
Serve LLMs locally and expose production-ready APIs.
Python CLI coding assistant
Build an agent-driven assistant with Claude API workflows.
Document RAG pipeline
Index, retrieve, and answer with embeddings and vector stores.
Queue-based scalable RAG system
Scale retrieval with Redis queues and async workers.
AI conversational voice agent
Ship real-time voice agents with STT and TTS.
Graph memory agent
Add durable memory with Neo4j and LangGraph.
MCP-powered AI server
Connect AI to tools using the MCP standard.
Who This Course Is For
The bootcamp supports a range of backgrounds and goals.
Complete beginners
Step-by-step path from Python basics to production AI.
Backend / Data engineers
Drop-in AI skills with agents, RAG, and LLMs in existing stacks.
Developers using APIs
Go deeper on queues, scaling, memory, and graph agents.
Students & professionals
Job-ready portfolio with eight real projects.
Schedule & Pricing
Live weekday sessions with a job-ready project portfolio.
45 Days Classes
Monday to Friday, 8:30 AM – 9:30 AM IST
Offer Price ₹29,999
Course fee ₹39,999. Limited seats for the current cohort.
Live Instructor-Led
Interactive classes with mentor support.
8 Capstone Builds
Real projects to showcase in interviews.
Ready to Become a Production-Ready AI Engineer?
Complete curriculum from Python foundations to agents, RAG, and MCP. Live weekday sessions with eight real-world AI projects.
Course Fee
₹39,999
Offer Price
₹29,999
Visit our Hyderabad office
Flat No.403, Nandini Residency, 15/A Addagutta Society - HMT Hills Rd, near JNTU, Addagutta Society, Jal Vayu Vihar, Kukatpally, Hyderabad, Telangana 500085
