.jpg&w=1536&q=75)
End-to-End AI Engineering Bootcamp
8 Weeks
·Cohort-based Course
Master end-to-end AI engineering - transform prototypes into production-ready apps with LLMs, RAG & agents in just 8 weeks.
This course is popular
3 people enrolled last week.
End-to-End AI Engineering Bootcamp
8 Weeks
·Cohort-based Course
Master end-to-end AI engineering - transform prototypes into production-ready apps with LLMs, RAG & agents in just 8 weeks.
This course is popular
3 people enrolled last week.
Course overview
🚀 Build Real AI Products, Not Just Prototypes
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.
Led by Aurimas Griciunas - LinkedIn Top Voice in AI, ex-CPO at Neptune.ai, and creator of the SwirlAI newsletter - you’ll gain deep, hands-on experience with Retrieval-Augmented Generation (RAG), agentic systems, memory, observability, and scalable deployments.
💡 Why This Bootcamp?
AI is no longer experimental. Businesses are investing in production-grade AI products, and teams need engineers who can go from idea to infrastructure, from prototype to product. This course is built to reflect that reality.
You won’t just learn what RAG or agents are - you’ll learn when to use them, how to build them, and why certain architectures scale better than others. You’ll move beyond demos and notebooks to ship a complete, working solution.
🛠️ What You’ll Build
You’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
✔️ LLM APIs (Gemini, Claude, GPT, etc.).
✔️ Vector databases & RAG.
✔️ AI agent libraries (LangChain, LangGraph, CrewAI).
✔️ Docker, FastAPI, Kubernetes, cloud deployment.
✔️ Observability, evaluation, and performance testing.
✔️ Modern communication protocols (A2A, MCP).
🧠 How It Works
Each week follows a real engineering sprint:
Sprint Lesson (Monday): Self-paced learning with videos, cheatsheets & reference code.
Sprint Review (Tuesday): Live walkthrough with Aurimas + deep Q&A.
Sprint Build Lab (Thursday): Live coding session to implement key sprint features.
Bonus QnA and Feedback sessions.
📌 Every concept you learn is applied directly to your project. No filler. No fluff. Just real-world engineering skills.
🏁 What You’ll Leave With
✅ A working AI product you can demo.
✅ The skills to scope, architect, and ship production AI systems.
✅ Deep confidence working with RAG, agents, and LLM infrastructure.
✅ A process-driven mindset to lead AI projects in real teams.
✅ A permanent access to learning materials of future cohorts.
This course is for data scientists, ML engineers, data engineers, and technical analysts who have foundational Python and ML knowledge
01
Data Professionals (Analysts & Scientists)
Looking to move beyond analysis and modeling to build and deploy real-world AI systems.
02
ML Engineers
Who want to deepen GenAI skills and master scalable, production-ready AI engineering from end to end.
03
Data Engineers
Ready to expand into AI by learning how to integrate data pipelines with LLMs, RAG, and agent-based systems.
What you’ll get out of this course
Build a robust Retrieval Augmented Generation (RAG) system from scratch to deliver context aware AI solutions.
Implement AI agents capable of tool integration, reflection, and autonomous decision making.
Master best practices for automated prompt engineering.
Deploy scalable, production-ready AI applications using containerization, and cloud infrastructure.
Establish best-practice LLMOps processes, including LLM observability, system monitoring and continuous evaluation.
Ensure security and compliance in AI solutions through practical strategies and real-world testing.
Learn how to apply emerging technologies such as Model Context Protocol (MCP) and Agent-to-Agent (A2A) communication protocol.
Showcase a fully operational AI application as a final capstone project, ready for professional portfolios.
What’s included
.jpg&w=1536&q=75)
Live sessions
Learn directly from Aurimas Griciūnas in a real-time, interactive format.
Lifetime access
Go back to course content and recordings whenever you need to.
Community of peers
Stay accountable and share insights with like-minded professionals.
Certificate of completion
Share your new skills with your employer or on LinkedIn.
Maven Guarantee
This course is backed by the Maven Guarantee. Students are eligible for a full refund up until the halfway point of the course.
Course syllabus
44 lessons • 8 projects
Week 1
Sprint 0 – Problem Framing & Infrastructure Setup
Week 2
Sprint 1 – Build the First Working RAG Prototype
Week 3
Sprint 2 – Retrieval Quality & Prompt Engineering
Week 4
Sprint 3 – Moving From Basic To Agentic RAG
Week 5
Sprint 4 – Agents & Agentic Systems
Week 6
Sprint 5 – Multi-Agent Systems
Week 7
Sprint 6 – Deployment, Optimization and Reliability
Week 8
Sprint 7 – Final Demo & Capstone Delivery
Bonus
Exclusive Bonus Toolkit
Meet your instructor
Aurimas Griciūnas
Aurimas Griciūnas
Aurimas Griciūnas is a recognized AI expert, LinkedIn Top Voice in AI, and the founder of SwirlAI. He previously served as Chief Product Officer at Neptune.ai where he worked closely with top ML teams to scale infrastructure, evaluation, and LLMOps practices across industries. With over a decade of experience at the intersection of data science, machine learning, and software engineering, Aurimas has led AI initiatives in both startups and enterprise environments. His mission is to bridge the gap between hype and reality by teaching engineers how to build systems that work in the real world. Students will benefit from his hands-on knowledge, technical depth, and product-first mindset - gained by solving actual engineering problems.
Join an upcoming cohort
End-to-End AI Engineering Bootcamp
Cohort Beta
$1,800
Dates
Application Deadline
Course schedule
10 hours per week
Live Events Tuesdays, Thursdays & Fridays
11:00am - 1:00pm EST
Lessons, hands-on coding, Q&A, and feedback.
Capstone Project
6 hours per week
Your capstone project is the core of this bootcamp. From Week 1, you'll define a real-world AI use case and develop it incrementally across sprints. Each concept - RAG, agents, memory, optimization—is applied directly to your project, preparing you to showcase your solution.
Learning is better with cohorts
Active hands-on learning
This course builds on live workshops and hands-on projects
Interactive and project-based
You’ll be interacting with other learners through breakout rooms and project teams
Learn with a cohort of peers
Join a community of like-minded people who want to learn and grow alongside you
Frequently Asked Questions
Stay in the loop
Sign up to be the first to know about course updates.
Join an upcoming cohort
End-to-End AI Engineering Bootcamp
Cohort Beta
$1,800
Dates
Application Deadline