.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.
.jpg&w=1536&q=75)
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 (OpenAI, Claude, etc.).
✔️ Vector databases & RAG.
✔️ AI agent libraries (LangChain, LangGraph, CrewAI).
✔️ Docker, FastAPI, cloud deployment
✔️ Observability, evaluation, and performance testing.
✔️ Modern communication protocols (MCP, A2A).
🧠 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.
This course includes
21 interactive live sessions
Lifetime access to course materials
44 in-depth lessons
Direct access to instructor
8 projects to apply learnings
Guided feedback & reflection
Private community of peers
Course certificate upon completion
Maven Satisfaction Guarantee
This course is backed by Maven’s guarantee. You can receive a full refund within 14 days after the course ends, provided you meet the completion criteria in our refund policy.
End-to-End AI Engineering Bootcamp
Course syllabus
Week 1
Jun
24
Sprint Review: Project framing, tooling overview, and repo setup
Tue 6/243:00 PM—5:00 PM (UTC)Jun
26
Sprint Build Lab: Set up development environment and scaffold project repo
Thu 6/263:00 PM—5:00 PM (UTC)Jun
27
QnA Session
Fri 6/273:00 PM—4:00 PM (UTC)
Jun
23
End-to-end AI Engineering bootcamp Prep
Sprint 0 – Problem Framing & Infrastructure Setup
Week 2
Jul
1
Sprint Review: Walkthrough of RAG structure and MVP objectives
Tue 7/13:00 PM—5:00 PM (UTC)Jul
3
Sprint Build Lab: Implement and evaluate your first end-to-end RAG pipeline
Thu 7/33:00 PM—5:00 PM (UTC)
Sprint 1 – Build the First Working RAG Prototype
Week 3
Jul
8
Sprint Review: Evaluation methods and automated prompt tuning
Tue 7/83:00 PM—5:00 PM (UTC)Jul
11
Sprint Build Lab: Improve context retrieval, prompts, and system robustness
Fri 7/113:00 PM—5:00 PM (UTC)
Sprint 2 – Retrieval Quality & Prompt Engineering
Week 4
Jul
15
Sprint Review: Moving from basic to agentic RAG
Tue 7/153:00 PM—5:00 PM (UTC)Jul
17
Sprint Build Lab: Build a tool-using agent integrated with your RAG backend
Thu 7/173:00 PM—5:00 PM (UTC)Jul
18
Optional: QnA Session
Fri 7/183:00 PM—4:00 PM (UTC)Optional
Sprint 3 – Moving From Basic To Agentic RAG
Week 5
Jul
22
Sprint Review: Autonomous agents
Tue 7/223:00 PM—5:00 PM (UTC)Jul
25
Sprint Build Lab: Agentic systems
Fri 7/253:00 PM—5:00 PM (UTC)Jul
21
Optional: Bootcamp Mid Feedback Session
Mon 7/219:00 AM—10:00 AM (UTC)Optional
Sprint 4 – Agents & Agentic Systems
Week 6
Jul
29
Sprint Review: Designing and orchestrating multi-agent workflows
Tue 7/293:00 PM—5:00 PM (UTC)Jul
31
Sprint Build Lab: Implement a multi-agent task flow and run coordination scenarios
Thu 7/313:00 PM—5:00 PM (UTC)
Sprint 5 – Multi-Agent Systems
Week 7
Aug
5
Sprint Review: Best practices for cloud deployment, monitoring, and performance tuning
Tue 8/53:00 PM—5:00 PM (UTC)Aug
7
Sprint Build Lab: Containerize your capstone and implement CI/CD
Thu 8/73:00 PM—5:00 PM (UTC)Aug
8
QnA Session
Fri 8/83:00 PM—4:00 PM (UTC)
Sprint 6 – Deployment, Optimization and Reliability
Week 8
Aug
12
Demo Day: Present your working AI product to cohort
Tue 8/123:00 PM—5:00 PM (UTC)Aug
14
Closing Celebration & Feedback
Thu 8/143:00 PM—5:00 PM (UTC)
Sprint 7 – Final Demo & Capstone Delivery
Bonus
Exclusive Bonus Toolkit
Meet your instructor
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 Alpha
$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 Alpha
$1,800
Dates
Application Deadline