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Cohort Starts Jul 15

Master AI Engineering |

Bridge the gap between theory and reality. Learn from engineers who have built scale. Industry-grade system design, production-ready code, and architectural patterns.

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₹13,999
Use coupon AIALGOCAMP for discount
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Trusted by engineers from Amazon, Google, Microsoft and more
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Built for Production

We don't teach ChatGPT wrappers. We build production AI systems — from RAG pipelines and agents to models deployed at real-world scale.

LLM Systems at Scale

Go beyond prompt engineering. Architect, fine-tune, and deploy models that power real products. > RAG Pipelines
> Vector Databases
> Model Serving & Inference

Intelligent Agents

Build autonomous systems that reason, plan, and execute across tools, APIs, and workflows. > Multi-step Workflows
> Tool Calling & MCP
> Memory & Context Management

Production AI Ops

Ship AI features with reliability, observability, and cost control — not just demos. > Evals & Guardrails
> Cost & Latency Tuning
> AI Observability

Cohort Starts Jul 15

Master AI Engineering

This isn't another "Todo List" tutorial. Build production-ready AI systems — from LLMs and agents to scalable AI infrastructure.

Join Master AI Engineering
₹13,999
Use coupon AIALGOCAMP for discount

Are you ready for Production?

Most developers can call an LLM API. Very few know how to ship a production AI system that handles hallucinations, cost spikes, and agent failures at scale.

If you can't answer the questions on the right, you are leaving money on the table in your career.

> Can you build a RAG pipeline with hybrid search and reranking?
> How do you evaluate LLM outputs with RAGAS and LLM-as-judge?
> Do you know how to implement function calling with Pydantic outputs?
> How would you deploy a voice agent with STT → LLM → TTS?
> Can you debug agent workflows with Langfuse or LangSmith tracing?
> How do you prevent prompt injection and implement guardrails in production?
> Can you build a RAG pipeline with hybrid search and reranking?
> How do you evaluate LLM outputs with RAGAS and LLM-as-judge?
> Do you know how to implement function calling with Pydantic outputs?
> How would you deploy a voice agent with STT → LLM → TTS?
> Can you debug agent workflows with Langfuse or LangSmith tracing?
> How do you prevent prompt injection and implement guardrails in production?
hands-on projects

AI Engineering Project Portfolio

A 6-month, project-driven program. Build, evaluate, deploy, and scale real-world AI products — from RAG and agents to deep learning foundations.

Claude Code–like Coding Agent

Autonomous agent that inspects codebases, edits files, and runs commands in a sandbox.

LangGraph MCP E2B Sandbox

Challenge: Build a Claude Code-style agent that understands a codebase, inspects files, makes edits, runs commands in a sandbox, and iterates on fixes.

  • • Codebase indexing and file inspection tools
  • • Sandboxed command execution with E2B
  • • Multi-step agent workflows with LangGraph
  • • Iterative edit-run-fix loops until tasks complete

Enterprise Advanced RAG

Enterprise-grade RAG with hybrid search, reranking, and RAGAS evaluation.

Hybrid Search Reranking RAGAS

Challenge: Build an enterprise-grade RAG stack with hybrid search, cross-encoder reranking, guardrails, and end-to-end RAGAS evaluation on real corporate data.

  • • Hybrid search combining BM25 and vector retrieval
  • • Cross-encoder reranking for precision
  • • NeMo Guardrails and output validation
  • • RAGAS metrics to measure and improve quality

Customer Support Agent

Classifies tickets, retrieves docs via RAG, and escalates when needed.

RAG Ticket Routing FastAPI

Challenge: Build a production-style agent that classifies tickets, retrieves help docs via RAG, drafts replies, and escalates to a human when the model is out of its depth.

  • • Ticket classification and intent detection
  • • RAG over help documentation
  • • Draft reply generation with guardrails
  • • Human-in-the-loop escalation workflows

RAG Chatbot over YouTube Transcripts

Pinecone-backed Q&A grounded in video transcripts with timestamps.

Pinecone Transcripts LangChain

Challenge: Build a Pinecone-backed RAG chatbot that ingests YouTube transcripts, retrieves relevant timestamps, and answers questions grounded in what was actually said.

  • • YouTube transcript ingestion pipeline
  • • Pinecone vector storage with metadata
  • • Timestamp-aware retrieval and citations
  • • Grounded answers with source references

AI Code Reviewer like CodeRabbit

Automated PR reviews with style-guide RAG and inline GitHub comments.

GitHub API PR Analysis Security Scan

Challenge: Build a CodeRabbit-style AI reviewer that reads every pull request, flags bugs and security issues with RAG over your style guide, and posts structured inline comments via the GitHub API.

  • • Diff analysis and bug detection
  • • RAG over team style guides and conventions
  • • Security issue flagging and severity scoring
  • • Structured inline comments via GitHub API

AI Web App Builder like Lovable / v0

Multi-agent prompt-to-app pipeline with live preview sandbox.

Multi-Agent Live Preview Prompt-to-App

Challenge: Build a prompt-to-app system where a user describes a web app and a multi-agent pipeline (planner → coder → reviewer) ships a working version with live preview.

  • • Planner agent for architecture and requirements
  • • Coder agent for component generation
  • • Reviewer agent for quality and bug checks
  • • Live preview sandbox with E2B
The Path to AI Engineer

Production Curriculum

A structured path from "Prompt Tinkerer" to "Production AI Engineer".

Phase 1: Foundation

Python & LLM Foundations

Python, LLM fundamentals, chatbots & prompt engineering.

  • Python Basics, OOP & Type Hints with Pydantic
  • LLM Mental Model: Tokens & Context Windows
  • OpenAI/Gemini API Setup & Sampling
  • Conversations, Roles & Multi-Turn History
Phase 2: LLM Applications

Structured LLM Apps & FastAPI

FastAPI LLM apps, function calling & vector search.

  • End-to-End LLM Application Structure
  • FastAPI Basics for AI Applications
  • Function Calling & Tool Use
  • Pydantic Structured Outputs
Phase 3: RAG Systems

RAG, Vector DBs & Hugging Face

RAG pipelines, Hugging Face & production retrieval.

  • RAG with LangChain & Hybrid Search
  • Enterprise Advanced RAG & Reranking
  • LlamaIndex for Production RAG
  • Scalable RAG with Async Queues (RQ/Redis)
Phase 4: AI Agents

Agents, LangGraph & MCP

LangGraph agents, memory, voice & MCP tools.

  • Multi-Modal AI: CV, Speech & CLIP
  • AI Agents with smolagents & Tools
  • AutoGen, CrewAI & Multi-Agent Patterns
  • LangGraph & Stateful Agent Workflows
Phase 5: Production AI

Deploy, Observe & Secure AI Systems

Deploy, observe, secure & govern AI systems.

  • Streaming (SSE), Retries & Fallbacks
  • Local LLM with Ollama & Docker
  • Prompt Caching & Cost Optimization
Phase 6: Deep Learning

PyTorch, Transformers & LLM Internals

PyTorch, transformers & LLM internals deep dive.

  • Math, PyTorch & Training Loops
  • CNNs, Batch Norm & Vanishing Gradients
  • RNNs, LSTMs & GRUs
  • Transformers from Scratch & BPE
Phase 7: Projects

Production-Grade AI Projects

Capstone projects across agents, RAG & deep learning.

  • Claude Code-like Coding Agent
  • Enterprise Advanced RAG
  • Customer Support Agent
  • RAG Chatbot over YouTube Transcripts
View Syllabus

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