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Design, build, deploy, and operate production-ready AI systems using LLMs, RAG, agents, APIs, and AI Ops practices.

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Founder & AI Instructor at School of AI

Vivian Aranha

AI architect with 2.2M+ learners, building enterprise-grade agentic systems

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Learn on your schedule with this self-paced course featuring pre-recorded sessions. Enjoy lifetime access, so you can revisit and reinforce your learning whenever needed.AI is moving from experimentation to real-world systems, yet most professionals face a major challenge: knowing how to move from models and prompts to production-ready AI applications.Many courses teach isolated concepts—prompting, machine learning theory, or model APIs—but very few explain how modern AI systems are actually built, deployed, and managed.This 7-Day AI Engineer Bootcamp is designed to close that gap.In this hands-on program, you will learn how AI engineers design and build real systems using LLMs, embeddings, retrieval-augmented generation (RAG), AI agents, APIs, and production deployment patterns.Instead of focusing only on theory, the course walks you through the complete AI engineering lifecycle: data, prompts, architecture, agents, deployment, monitoring, and scaling.By the end of the program, you will build a complete AI system, such as a customer support agent, research assistant, or knowledge bot, and understand how to deploy and manage AI applications in real environments.

Build Production-Ready AI Systems in 7 Days — From LLMs to Agents

Build AI-Powered Applications with LLMs

Implement Retrieval-Augmented Generation (RAG)

Create Autonomous AI Agents

Deploy AI Systems as APIs

Operate Production AI Systems

Design End-to-End AI Architectures

Software Engineers exploring AI development Developers who want to build real AI systems using APIs, agents, and modern AI frameworks.

Product managers and technical founders Professionals who want to understand how AI products are designed, built, and deployed.

Data professionals transitioning into AI engineering Analysts &amp; data scientists who want to expand into building end-to-end AI applications.

Learn to design, build & deploy real AI systems—become a portfolio-ready AI builder, not just a prompt user.

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Most AI courses teach you prompts. This course teaches you how to build real AI systems.Right now, professionals face a critical challenge: AI is everywhere — but very few people know how to turn it into working, deployable applications. Watching demos isn’t enough. Copy-pasting prompts isn’t enough. Employers and clients want people who can design, build, evaluate, and deploy AI-powered solutions that actually solve problems.If you’ve felt overwhelmed by the noise — new tools every week, agent frameworks, RAG, automation — you’re not alone. The real gap isn’t intelligence. It’s structure.This course matters because it gives you a complete, end-to-end mental model of modern AI systems. In 14 days, you move from understanding how AI works to deploying your own AI-powered application with memory, tools, automation, and evaluation.By the end, you won’t just “know AI.” You’ll have built and shipped a real product — and that changes your confidence, credibility, and career trajectory.Learn on your schedule with this self-paced course featuring pre-recorded sessions. Enjoy lifetime access, so you can revisit and reinforce your learning whenever needed.

Build Real AI Systems & Agents in 14 Days — Not Just Prompts

Build End-to-End AI Systems

Work Confidently with LLM APIs

Engineer Reliable Prompts & Outputs

Implement RAG & AI Memory

Build Task-Executing AI Agents

Deploy & Present Portfolio Projects

Aspiring AI builders who want real projects, not just prompt tricks, and need a structured path to ship their first AI app.

Developers or engineers curious about AI who want to add LLMs, agents, and automation to their existing skill set.

Product managers &amp; tech professionals who want to understand AI systems deeply enough to build, deploy, and lead AI initiatives.

Learn to build and deploy real AI apps using LLMs, RAG, and agents—transforming from AI user to production-ready AI engineer.

This course is completely asynchronous and available to view at your own convenience. There will be no Live sessions.AI is transforming software development, but many professionals struggle to move beyond tutorials and actually build real AI applications. Most courses focus on theory or simple prompts, without teaching how to design complete AI systems.This bootcamp bridges that gap.In three intensive days, you will learn the core capabilities of modern AI engineering: working with LLM APIs, prompt engineering, Retrieval-Augmented Generation (RAG), AI agents, and deployment. Instead of just learning concepts, you will build real applications step by step—from your first AI-powered app to a document-aware RAG system and an autonomous AI agent.By the end of the program, you will have designed, built, and deployed a production-ready AI application. You’ll leave with practical skills, a portfolio project, and the confidence to build intelligent AI systems for real-world use cases.Learn on your schedule with this self-paced course featuring pre-recorded sessions. Enjoy lifetime access, so you can revisit and reinforce your learning whenever needed.

Build Production-Ready AI Apps with RAG, Agents, and Real Deployment

Build Your First AI Application with LLM APIs

Master Prompt Engineering for Reliable AI Outputs

Design and Build a Retrieval Augmented Generation (RAG) System

Build AI Systems with Tools and External Data

Create Autonomous AI Agents that Perform Tasks

Deploy a Production-Ready AI Application

Software Developers Transitioning into AI Engineering Want to build AI-powered applications using LLM APIs, RAG systems, and agent workflows.

Data Scientists Expanding into AI Application Development Want to move beyond models and build real AI products and deploy production systems

Tech Professionals and Builders Creating AI Products PMs, founders, and engineers who want to build and launch practical AI applications.

Move from basic prompting to building production-ready AI systems using GPT, Claude, Mistral APIs with copilots, RAG, model orchestration

Build Production-Ready AI Systems

Data Science Academy

AI educator helping engineers build real-world generative AI systems

Generative AI has moved far beyond simple prompts and chatbots. Companies now need engineers and developers who can build complete AI systems — applications that combine multiple models, APIs, reasoning pipelines, memory, tools, and external data.However, most courses only teach prompt engineering or isolated API usage. They rarely show how real AI systems are designed, orchestrated, evaluated, and deployed in production environments.This course closes that gap.You will learn how modern LLM ecosystems work across OpenAI, Anthropic, and Mistral, and how to build real applications using their APIs. Instead of theory alone, we focus on practical system design, including structured outputs, function calling, reasoning workflows, multi-model orchestration, and retrieval-augmented generation (RAG).Through hands-on labs and projects, you will build real AI utilities such as copilots, API-driven applications, hybrid model pipelines, and knowledge-aware assistants. By the end of this program, you will move beyond basic prompting and gain the skills needed to design, build, and deploy modern generative AI systems used by real products and companies today.

Learn to Build Production-Ready Generative AI Systems & LLM Applications

Design production-ready generative AI applications

Master prompt engineering and context management

Build AI tools using APIs, function calling, and structured outputs

Develop AI copilots and real-world AI utilities

Create multi-model AI pipelines and orchestration systems

Integrate memory, RAG, and production evaluation

Developers who want to build real LLM applications, AI copilots, and multi-model systems using GPT, Claude, Mistral, and modern APIs.

Engineers looking to integrate generative AI into products using APIs, function calling, RAG pipelines, and production AI workflows.

Product builders and startup founders who want to design and launch AI-powered tools, copilots, and intelligent automation systems.

Learn how to design, build, and deploy autonomous AI agents using modern frameworks and real-world agent architectures.

AI Engineering & Agent Systems

Building real-world AI systems, agents, and production ML architectures.

AI is rapidly moving beyond simple chatbots and prompt-based tools. The next generation of AI systems are autonomous agents that can reason, plan tasks, use external tools, retrieve knowledge, and collaborate with other agents to complete complex work.However, most developers and AI practitioners still struggle to understand how to design and engineer these systems in practice. Tutorials often focus on isolated demos, but they rarely teach the architecture, workflows, and engineering patterns required to build reliable agent systems.This course bridges that gap.You will learn how to design real agentic AI architectures using modern frameworks such as LangChain, LangGraph, RAG pipelines, vector databases, and the Model Context Protocol (MCP). Instead of just experimenting with prompts, you will understand how to build stateful agents, multi-agent systems, tool integrations, and production-ready AI workflows.By the end of the course, you will have a clear mental model of how modern AI agent systems are designed, orchestrated, and deployed, giving you the skills to build next-generation AI applications and automation systems.

Build real AI agents and production-ready autonomous AI systems

Design real-world AI agent architectures

Build agents that use tools, APIs, and external systems

Implement memory and knowledge systems for agents

Create collaborative multi-agent systems

Integrate AI agents using Model Context Protocol (MCP)

Deploy production-ready AI agent systems

Developers who want to build AI agents, tool integrations, and autonomous workflows using modern AI frameworks.

Professionals working with LLMs or ML who want to learn agent architectures and production AI systems.

Tech builders and founders interested in creating AI agents, multi-agent systems, and intelligent automation.

Master Generative AI engineering by building production-ready LLM systems using RAG, agents, embeddings, and scalable AI architectures.

Training Engineers in AI & Data Science

Industry-focused academy training engineers in AI, LLMs, and data science.

Generative AI is transforming every industry — but most engineers only learn how to use AI tools, not how to build real AI systems.Companies are urgently looking for engineers who can design production-ready LLM applications, implement Retrieval-Augmented Generation (RAG) pipelines, build AI agents, and integrate AI into real software products.This course bridges that gap.You will learn how to move from AI experimentation to real engineering, building systems that combine LLMs, embeddings, vector databases, APIs, and agents into scalable applications.By the end of this course, you will know how to:• Design reliable prompts and LLM workflows • Build semantic search systems using embeddings • Implement production-ready RAG pipelines • Develop full-stack LLM applications • Optimize systems for cost, performance, and scaleInstead of just learning theory, you will build the core architecture behind modern AI products used by startups and tech companies today.If you want to become an AI Engineer capable of building real Generative AI systems, this course will give you the practical foundation to do it.

Learn to Build Production-Ready Generative AI & LLM Applications

Understand how transformers and LLM architectures work

Design reliable prompts and structured LLM workflows

Build semantic search systems using embeddings and vector databases

Develop Retrieval-Augmented Generation (RAG) systems

Create AI agents and tool-enabled LLM systems

Build and deploy full-stack LLM applications

Software engineers who want to transition into AI engineering and build production-ready LLM and generative AI applications.

Data scientists and ML engineers looking to learn RAG, agents, embeddings, and modern LLM application development.

AI builders, startup founders, and product developers who want to integrate generative AI into real-world products.

AI Fundamentals

Apply AI in your role

Career Development

AI Fundamentals

Career development

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AI Fundamentals

Career development

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AI Fundamentals

Career development

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AI Fundamentals

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AI Fundamentals

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