School of AI

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What It Takes to Become an AI Engineer — Live Breakdown

Vivian Aranha

Founder and AI Instructor, School of AI

Learn the four-layer AI engineering model and how each layer fits into real-world systems.

Understand the AI Engineer Skill Stack

Walk through data, models, APIs, and deployment to understand how AI works in production.

See a Real AI System Built End-to-End

Get a clear roadmap of projects, tools, and habits that turn AI knowledge into real skills.

Learn the Fastest Path to Become Job-Ready

Master end-to-end AI engineering - transform prototypes into production-ready apps with LLMs, RAG & agents in just 8 weeks.

Master Cohort

Founder @ SwirlAI • Ex CPO @ neptune.ai

neptune.ai

Aurimas Griciunas

LinkedIn Top Voice in AI • Founder & CEO @ SwirlAI

Cohort 4

Cohort 5

The End-to-End AI Engineering Bootcamp is an 8-week, cohort-based experience designed to turn technical professionals into full-stack AI engineers who can confidently design, build, and deploy production-grade AI systems.🛠️ What You’ll BuildYou’ll develop your own capstone project - a real-world AI application built sprint by sprint, applying each week’s concept to solve a business-relevant use case. By the end, you’ll present it live on Demo Day, with a working repo and deployed app you can showcase to hiring managers, CTOs, or investors. 🧑‍💻Technologies include:<ul><li>LLM APIs (Gemini, Claude, GPT, etc.).</li><li>Vector databases &amp; RAG.</li><li>AI agent libraries (LangChain, LangGraph, ADK, OpenAI Agents SDK).</li><li>Docker, FastAPI, Kubernetes, cloud deployment.</li><li>Observability, evaluation, and performance testing.</li><li>Communication protocols (A2A, MCP).</li></ul>🧠 How It WorksEach week follows a real engineering sprint:<ul><li>Sprint Lesson (Monday): Self-paced learning with videos, cheatsheets &amp; reference code.</li><li>Sprint Review (Tuesday): Live walkthrough with Aurimas + deep Q&amp;A.</li><li>Sprint Build Lab (Thursday): Live coding session to implement sprint features.</li><li>Bonus QnA and Feedback sessions.</li></ul>🎬 Pre-course that will help you prepare <a target="_blank" rel="noopener noreferrer nofollow" href="https://youtube.com/playlist?list=PLeId95Z-0iyOATuFuHt3zP5enX3eCOsoq&amp;si=1xrntQdNOUkXwH6H">here</a>.

🚀 Build Real AI Products, Not Just Prototypes

Design and optimize RAG architectures

Engineer and orchestrate agentic systems

Design and deploy multi-agent systems for complex workflows

Implement structured prompt and context management

Apply LLMOps for observability and continuous evaluation

Build and deploy production-grade GenAI applications

Software Engineers &amp; DevelopersWho want to transition into AI Engineering and build, deploy and scale real-world AI systems end-to-end.

ML Engineers &amp; Data ScientistsWho want to architect, evaluate and scale production-grade agentic AI systems.

Technical Founders &amp; CTOsWho want to make AI a core product capability and confidently design and ship scalable AI systems.

By the end of this course, you’ll transition from a software engineer to a confident AI engineer—able to identify the right problems, design

AI Engineer & Consultant for F-500 firms

Qualcomm

Wipro

Sujeet Mishra

AI Engineer | Entrepreneur | AI Consultant & Trainer for Fortune-500

AI/ML | Search | Ex-Microsoft, Yahoo

Apple

Microsoft

Yahoo

Prasenjit Mukherjee

Adjunct Faculty | Quantum ML & Big Data | Ex-Microsoft, Yahoo | IIT Madras

Cohort 1

Cohort 2

For software engineers, moving into AI engineering is not just about learning new tools—it’s a mindset shift.Over the past year, we’ve worked with engineering teams, from Fortune 500 companies to unicorn startups, helping them adopt AI-native and agent-based systems. One pattern shows up repeatedly: engineers don’t struggle because they can’t build, but because AI systems behave very differently from deterministic software.⚙️ Engineers must learn how to design, evaluate, and operate probabilistic systems, reason about failures and trade-offs, and think end-to-end—from use-case identification to production and scale.Many engineers hesitate because they believe AI engineering requires deep ML or statistics expertise. In reality, modern AI engineering is about system design, evaluation, observability, and production readiness—skills engineers already have but need to apply in a new context.🚀 This course provides a practical 6-week roadmap to make that transition by building and extending a real, production-grade agentic system.By the end, you won’t just understand AI systems—you’ll think and operate like an AI engineer.For queries, write to us: ai-education@theseuslab.ai

A 6-week roadmap for software engineers to become production-ready AI engineers

Use LLMs to 10x day-to-day engineering productivity

Design and build agentic systems using LangChain/LangGraph

Build reliable agentic systems by designing for evaluation and observability

Take AI ideas from use-case discovery to production and scale

Apply everything you learn to a real, end-to-end capstone project

Basic understanding of SDLC concepts like databases, APIs, frontend/backend systems, and servers

Familiarity with Software Engineering Fundamentals

Some programming experience, even if not recent—or willingness to spend ~8 hours pre-course to learn Python (we will provide resources)

Prior Programming Exposure (or Willingness to Ramp Up)

Openness to learning, experimenting, and thinking differently

Curiosity and Willingness to Experiment

Software engineers looking to transition into AI engineering roles and build production-grade AI systems

AI engineers and data science professionals who want to get a hands-on experience of building, evaluating and scaling agentic AI applications

Product managers and Tech leaders seeking a practical, system-level view of how production-ready AI applications are designed and shipped

The skills you need to succeed as an AI Engineer in production environments.

Owner/CEO, AI Makerspace

AI Makerspace

AI Fund

FourthBrain

University of Dayton

Dr. Greg Loughnane, Ph.D.

Building industry-leading AI training for engineers and teams since 2021

Engineer @ Vercel l Evalite Maintainer

Can Temizyurek

Engineer @ Vercel, maintainer of Evalite (200k downloads per week)

Principal AI/ML Engineer @ Rumble

Stanford University

Multiverse Computing

AoCubo Imobiliária

Raul Salles de Padua

Principal AI & ML Engineer @ Rumble

TL;DR: <a target="_blank" rel="noopener noreferrer nofollow" href="https://bit.ly/AIE-v1-curr">find all curriculum details for v1.0</a> and the <a target="_blank" rel="noopener noreferrer nofollow" href="https://aimakerspace.io/the-aie-bootcamp-changelog/">changelog</a> since 2024.Today, the most important conversations in tech are about what to build with agents and why. Expert AI Engineers walk into those rooms and know they have earned a seat at the table.Developing the deep expertise at the edge of agentic AI to achieve this is easy in theory, yet quite difficult in practice.You must root yourself in both core conceptual patterns and practical code execution, both of which are evolving in real time.Over 10 weeks, you will routinely build 🏗️, ship 🚢, and share 🚀 with best-practice tools. Through accountability, repetition, and community, you will develop agentic AI expertise and achieve your personal career goals.Just ask our <a target="_blank" rel="noopener noreferrer nofollow" href="https://aimakerspace.io/graduates">Certified AI Engineer Graduates</a>.This course is designed for production backend engineers.All applicants must demonstrate their ability to dig into coding tools and build something with them by completing <a target="_blank" rel="noopener noreferrer nofollow" href="https://aimakerspace.io/aie-challenge">The AI Engineer Challenge</a>.Certifications remain active for two years. Recerts are 80% off. For all discount opportunities, see our <a target="_blank" rel="noopener noreferrer nofollow" href="https://bit.ly/aie-v1-discount-policy">discount policy</a>.AI Makerspace is the only certified enterprise training partner of <a target="_blank" rel="noopener noreferrer nofollow" href="https://www.linkedin.com/posts/a-meaningful-milestone-today-for-ai-makerspace-ugcPost-7460037108691476481-0wES?utm_source=share&amp;utm_medium=member_desktop&amp;rcm=ACoAAAOfOE4BdOGPD97rGOwB0qdCpNDB_wvyxsI">Andrew Ng &amp; Kirsty Tan's AI Aspire</a> on Maven.

The industry-standard certification for production AI Engineers.

Prototype and deploy many agent applications

Create effective evaluations (a.k.a. "evals")

Build full stack solutions to deliver complete user experiences 

Understand the foundational design patterns of AI Engineering

Attend to LLM Engineering

Apply production best-practices

This will be a place where you will hang out for many, many hours over the next 10 weeks.

Understand how to write Python code in an Interactive Development Environment

If you don't have a clear goal that aligns with completing your AIE Certification, you will not. Lifetime certification rate = 51.2%.

Have a clear goal of what you want to accomplish in your career in 2026

Backend software engineers and developers who want to build, deploy, operate, and improve agents in production environments.

The very best ML engineers or frontend developers who are pseudo-backend developers and understand what they must learn to stay relevant

Only the most ridiculous technical product managers; who somehow believe that getting deep into practical code execution will help them

Learn to design, monitor, and deploy working AI systems. This transforms them to build robust, real-world LLM apps using prove

May AI Engineer

CEO Hyperskill | Lecturer JetBrains, MIT

Nikolay Vyahhi

I'm passionate about using AI to make learning smarter and more accessible.

CTO Hyperskill | Founder TheOna 

Ruslan Davletshin

I’ve built and deployed LLM systems at scale in production environments. 

AI, ML Engineer & Teaching Expert

Nikalina Ogorodova

I enjoy bridging the gap between complex technology and clear understanding.

AI Engineer & Researher

Ivan Rodin

I have over 7 years of experience in Data Science and Machine Learning.

Bootcamp cuts through AI noise — from foundations to evaluating, securing, and deploying AI systems

June AI Engineer 

Are you an experienced engineer looking to build systems on top of LLMs, not just experiment with AI tools out of the box?In 2026, working with AI increasingly means designing and shipping products powered by LLMs: assistants, RAG pipelines, agentic workflows, evaluation systems. Many engineers use these tools but far fewer know how to structure them into reliable, production-grade systems.This program focuses on that 2026 skillset.The Hyperskill AI Engineering Bootcamp is a 10-week, hands-on program for engineers who want to move to architecting reliable AI systems. You’ll build end-to-end projects using LLMs as components: designing retrieval pipelines, working with vector databases, implementing agents, setting up evals and handling deployment, monitoring and security.The emphasis is on practical system design, trade-offs and failure modes.Modules include: • LLM pipelines &amp; system design • Agents and multi-agent patterns • Vector search &amp; RAG systems • Evaluation, monitoring, security • Deployment of LLM-based applicationsExpect ~10 hours/week of focused work.

10-week Agentic  AI Engineering from experienced educators & practitioners

Design and build functional LLM pipelines end-to-end

Build AI agents with real autonomy using tools, memory, and function calling

Design high-quality context & RAG systems

Build advanced multi-agent & multimodal AI systems

Deploy secure and privacy-first LLM applications

Evaluate, test & monitor AI systems

Comfortable with programming know some Python. New to Python? We’ll give prep materials and 1–2 weeks of free Hyperskill access.

Basic programming experience (Python preferred)

On average learning takes 10–12 hours weekly completing hands-on tasks, projects, and practice at their own pace

Weekly time commitment (10-12 hours)

Live sessions are held in small groups with flexible timing and typically run twice

Availability for live sessions

Software engineers who want to level up from basic LLM usage to building production-ready AI systems, agents, and RAG pipelines

Technical PMs and team leads who want to raise their company’s AI capabilities and confidently drive adoption of modern AI systems

Developers looking to switch jobs by gaining the modern, in-demand AI engineering skills companies hire for today

What It Takes to Become an AI Engineer — Live Breakdown

What you'll learn

Why this topic matters

You'll learn from

Vivian Aranha