AI Engineer Bootcamp
Learn to build secure AI solutions companies need
100+ hours of practice with live consultations, weekly portfolio-ready AI projects and code reviews 
We as developers need to be learning, learning, learning, and the current top topic is AI. So developers that want to move into this world — this bootcamp is the best choice.
Who it's for and what you'll be able to do
Which Python skills do you need to be an AI engineer?
Get the list of Python topics you need to become an AI engineer and get through the bootcamp. Check which ones you already know.
Your 10-week bootcamp learning path
Work with OpenAI API, write a CLI chat, build an assistant with retrieval, function calling and visualization. Create an automated analysis pipeline using an MCP server to process and visualize real-time data.
Automate reports and analysis that take your team hours of manual work.
Build a FastAPI backend with multiple agents + GitHub PR Review Agent using LlamaIndex and GitHub Actions.
Hand routine work like code review to agents, so engineers spend their time on harder problems.
Build a FastAPI application on Qdrant: data loading, indexing, vector search, optimization.
Make company documents and data searchable by meaning, not only by exact keywords.
Progress from vector search to RAG: build end-to-end pipeline, then implement query rewriting, reranking, HyDE, and data-source routing.
Give employees and customers answers grounded in your own data, with fewer made-up answers.
Set up Langfuse and Ragas, collect metrics, track tokens, implement NeMo guardrails, cost-limits, and LLM-API proxy. Implement caching of chat history with Redis.
Keep LLM costs predictable and catch data leaks and unsafe outputs before users see them.
Package everything in Docker, deploy to AWS, manage environment variables and logs, demonstrate production version.
Turn a prototype into a service your team can run, maintain and scale.
Design or build your own AI tool solving a real problem you care about. Share your project with a room of students and AI engineers, and get practical input on your idea, implementation, and next steps.
Leave with a working AI tool for a real problem at your company.
Update or create your CV and send it to the Hyperskill HR team for feedback on what's working and what to improve. Then book a mock interview with an HR representative and get detailed feedback on your responses.
Go into your job search with a reviewed CV and a practice interview behind you.
Case studies & examples of what you'll learn to build
after the bootcamp
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AI Engineer Bootcamp reviews
The most valuable part for me was the project-based approach. Completing real projects that I can publish and keep in my GitHub portfolio is by far the best outcome—tangible, motivating, and directly useful for career growth.
Happy balance between established software paths and new paths of AI. Felt very update to the minute with key guidance during weekly meetings.
AI helps me be more productive at work and in life. Deep-dives into new concepts were fantastic, and I even created a project that could help JetBrains PMs make better decisions. Those who embrace AI will have a clear advantage moving forward.
A major personal outcome was being able to immediately apply what I learned to a real-world work project. While I’m still refining and experimenting with it, the bootcamp gave me the architectural blueprint and technical toolkit—from RAG pipelines to multi-agent design—needed to architect and start building the application with confidence.
The focus on platform-independent agents and self-hosted LLMs turned out to be essential for me — especially because I work with confidential financial data that requires full control and secure deployment.
I like the material; it is a great chance to familiarize myself with AI tools. The proportion between Math and non-math tasks is just right. I also enjoy practical projects and exercises. Community helps with my questions and supports my motivation.
I can deal with RAG systems, embeddings, vector databases, LLM integration and evaluation, and multi-agent systems. I understand practical AI application development.
Building agentic workflows and working with real-world use cases helped me better understand how LLMs, agents, tools, and orchestration can be applied to enterprise problems.
The curriculum is very intense and consistent, with topics building on each other and reinforced through practical exercises that require knowledge of the previous material. The program can be roughly divided into two parts. The first part covers the classical applications of ML and LLMs, such as LLMs with tools, RAGs, data filtering, embeddings into vector databases, LLM evaluation, LLMOps, and more. The second part is more advanced, focusing on model architectures, fine-tuning, and model compression. Recently, there was an AI/IT conference in my country featuring lectures on AI and LLM applications. Looking at the program, I thought: “Come on, I know and have practiced every single topic!” Besides the strong learning experience, the Hyperskill team also helps you polish your CV and prepare for interviews. Hard study, but a wonderful experience!
The bootcamp lasted about 10 weeks, and during that time I was able to carry out and complete many projects. The modules were well-structured, and the sprint-based workflow was well-organized and enjoyable. The frameworks used are closely aligned with current market trends, which makes the program very interesting. I found the variety of products and the focus on operating an AI application to be particularly effective.
I had a very positive experience with Hyperskill’s AI Engineering Bootcamp. The program is well-structured, practical, and high quality overall. The good news is that the curriculum gave me a solid roadmap, a lot of high-quality material to keep studying in depth, and several new ideas for my own projects/startup. It also reinforced that transitioning toward an AI Engineering profile is realistic and achievable.
The team behind Hyperskill Bootcamps
I'm passionate about using AI to make learning smarter and more accessible. At Hyperskill, we're building the future of education, and I'm excited to be a part of it every day.
I’ve built and deployed LLM systems at scale in production environments. This training teaches the exact mindset and tools I’d expect from my engineering team.
I enjoy bridging the gap between complex technology and clear understanding. At Hyperskill Training, I help make advanced concepts accessible to learners.
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When designing this bootcamp, we kept asking ourselves what developers need to thrive in AI today and build confidence and momentum through real projects.
I have over 7 years of experience in Data Science, Machine Learning, Deep Learning, and their applications, experienced in working on complex projects like the one we've built with Intel Labs.
With 10+ years blending Software Engineering and UX Design skills and knowledge, I architect and build reliable and valuable AI Agents and LLM-powered apps.
Partnering with the best
Hyperskill AI Engineer Bootcamp vs. competitors
Complete learning package for your 2026
Reach out to discuss your individual offer or book a call
Watch a free webinar to find out if this bootcamp is for you
What is an AI Engineer and why does the market need one?
Frequently asked questions
The bootcamp requires around 10-12 hours of study per week, so it can be combined with a full-time job. The program is designed for working professionals.
We're happy you're with us! You get a discount on the bootcamp price up to $200 depending on your subscription plan. For exact numbers in your case, please contact our Educational Manager in the chat and we’ll calculate everything for you.
Absolutely. Reach out to our Educational Managers in chat and we’ll share an offer PDF to help you present the bootcamp to your employer.
Both. Live sessions every 1-2 weeks, daily async support, full materials and recordings available throughout and after the program.
Yes, we provide OpenAI tokens and credits via LiteLLM for the duration of the program. All deployment infrastructure works on free tiers (AWS, Qdrant Cloud, etc.).
This bootcamp moves fast and covers technical topics, so it's best suited for people with any development experience specifically (but not necessary) with Python. Feel free to review full prerequisites list.
If you’re unsure whether this bootcamp is a good fit for your background, please contact our Educational Manager in the chat, we'll be happy to help.
We estimate that this program can be completed in about 10 weeks, plus 1 extra week for a break if holidays occur (Christmas, Easter or any other long-lasting public US or European holidays). We understand that each learner's pace may vary, however, most learners are expected to dedicate 10-12 hours per week to their studies.
Our program is highly practice-oriented and teaches how to build AI systems around them: backend, retrieval, monitoring, deployment. This is a fullstack AI engineering that is supported with real-life cases and live consultations. Everything is done to upskill you to an AI engineer.
Behind this bootcamp there is a fast-paced, module-based program with new content. Unlike Hyperskill’s self-paced courses, it includes prerecorded webinars, live sessions, expert mentorship, career guidance and weekly coding sprints focused on building real AI products. You’ll get support, feedback and hands-on experience starting module 1.
Of course, you can download the full program here.
If you have any additional questions about the program, please reach out to us on Crisp or schedule a call with our educational manager.
The list price is $2,499. With the code AUTUMN the October 12 cohort costs $2,299. You can pay in full or in 3 payments, and US residents can use Klarna installments. The price includes OpenAI tokens and credits for the whole program, 1 year of Hyperskill Premium and a certificate. Hyperskill Premium subscribers get up to $200 off, and a full refund is available until the end of the first week.
The next cohort starts on October 12, 2026 and runs for 10 weeks. To enroll, click Join now and complete the payment on the checkout page. If you want to check whether the program fits you first, book a call with our team. Cohorts are kept small so that every project gets a code review, so seats are limited.
You can request a full refund up until the end of the first week of studies. After that, a refund is impossible. For additional information and the refund schedule, please reach us in a chat or book a call for more details.
Our instructors will conduct live sessions, assist you with project issues and complex topics, review your projects, and more. Your dedicated instructor will be available daily to answer your questions via a text chat daily and on live sessions weekly/bi-weekly.
Yes, upon successful completion of this program, you will receive a certificate from Hyperskill. You will receive a certificate of completion for the program or a certificate of participation depending on your progress. You can easily add these certificates to your LinkedIn profile or show it to your employer to showcase your accomplishments.
Most learners build their core AI engineering foundation within the first 40 hours of focused practice (approximately 3 weeks at 10-12 hours per week).The full program runs for 10 weeks to ensure production-level depth and real-world project experience.
That’s completely fine. Most developers don’t fully understand these concepts when they first encounter them. Terms like reasoning loops, RAG pipelines or multi-agent orchestration are part of modern AI system design, and they’re exactly what we break down step by step during the bootcamp. You’re not expected to know them now. Over the next 10 weeks (10-12 hours per week), you’ll implement them in real projects.
We don’t guarantee job placement, but our graduates report that recruiters specifically ask about LLM-based projects. By the end of the bootcamp, you’ll have 5 GitHub repositories to share with recruiters hiring for AI Engineering roles — a solid, practical portfolio.
Sure! You can read some of our graduates’ stories on the Hyperskill Blog page.
For full details on how the program works, including payments, refunds and participation terms, please refer to our Terms of Use.