Agentic AI Engineering 2026 Edition
Nikolay Vyahhi
CEO Hyperskill | Lecturer JetBrains, MIT
Ruslan Davletshin
CTO Hyperskill | Founder TheOna
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10-week Agentic AI Engineering from experienced educators & practitioners
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 & system design
• Agents and multi-agent patterns
• Vector search & RAG systems
• Evaluation, monitoring, security
• Deployment of LLM-based applications
Expect ~10 hours/week of focused work.
What you’ll learn
Learn to design, monitor, and deploy working AI systems. This transforms them to build robust, real-world LLM apps using prove
Apply prompting and pipeline design patterns to control model behavior and reliability.
Build LLM-powered apps using APIs, LangChain, and structured workflows.
Identify where LLM solutions work best and choose the right approach for each task.
Implement function calling and tool use to give agents real-world execution capabilities.
Design workflows vs agents, work with MCP servers, and build multi-agent systems.
Build an AI reviewer agent that analyzes PRs, validates code, and posts reviews automatically.
Apply context engineering: chunking, few-shot prompting, multi-shot, formatting, and memory.
Build RAG systems with Qdrant: semantic search, filtering, re-ranking, HyDE, agentic RAG.
Implement context compression, pruning, and multi-turn context management for consistency.
Use design patterns: orchestrator, planner, routing agents, human-in-the-loop workflows.
Build multimodal agents capable of reasoning over text, images, UI, and code.
Develop a multi-agent testing system that automates interface tests and coordinates humans.
Deploy local LLM apps using FastAPI, Docker, vector search, caching, and routing.
Apply privacy and compliance principles: PII detection, guardrails, prompt-injection prevention.
Optimize spend using LiteLLM, caching strategies, model routing, and self-hosted models.
Design evals for components and end-to-end systems using clear metrics and criteria.
Apply eval-driven development and build feedback loops with the data flywheel approach.
Monitor production agents using LangSmith, LangFuse, and system-level dashboards.
Learn directly from expert instructors
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Nikolay Vyahhi
I'm passionate about using AI to make learning smarter and more accessible.
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Ruslan Davletshin
I’ve built and deployed LLM systems at scale in production environments.
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Nikalina O.
I enjoy bridging the gap between complex technology and clear understanding.
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Ivan O.
I have over 7 years of experience in Data Science and Machine Learning.
Who this course is for
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
Prerequisites
Basic programming experience (Python preferred)
Comfortable with programming know some Python. New to Python? We’ll give prep materials and 1–2 weeks of free Hyperskill access.
Weekly time commitment (10-12 hours)
On average learning takes 10–12 hours weekly completing hands-on tasks, projects, and practice at their own pace
Availability for live sessions
Live sessions are held in small groups with flexible timing and typically run twice
What's included
Live sessions
Learn directly from your instructors in a real-time, interactive format.
Maven Guarantee
Your purchase is backed by the Maven Guarantee.
Course syllabus
7 live sessions • 33 lessons • 3 projects
Week 1
Mar
23
Intro Call: Bootcamp Kickoff & Onboarding
Mon 3/235:00 PM—6:00 PM (UTC)
Module 1: Introduction to LLMs and Pipelines
🧑🏫 Weekly Office Hours
Project: The Stock Analyzer (Responses API)
Week 2
Apr
2
Weekly Sync: Progress, Plans & Questions
Thu 4/24:00 PM—5:00 PM (UTC)
Module 1: Continue Intro to LLMs & Pipelines
Project: Simple Python CLI Chat
Project: Introduction to Langchain (Bonus project)
Free resource
Workshop - AI Agents: Beyond the Hype
Get a clear, no-hype understanding of AI agents.
In this free guide, you’ll learn:
• What AI agents really are — and the myths holding teams back
• How agents work in practice: memory, tools, goals, control loops
• When to choose a single agent vs. a multi-agent setup
• Key tradeoffs: autonomy vs. control, flexibility vs. reliability
Hosted by Ivan Rodin — AI researcher with experience at Intel Labs and Philips Research, and author of courses on neural networks, LLMs, and prompt engineering.
Schedule
Live sessions
2-3 hrs / week
Mon, Mar 23
5:00 PM—6:00 PM (UTC)
Thu, Apr 2
4:00 PM—5:00 PM (UTC)
Thu, Apr 9
4:00 AM—5:00 AM (UTC)
Projects
5-7 hrs / week
Async content
1-2 hrs / week
Testimonials
- This bootcamp gave me insights into data science, and I understood that LLMs are not universal. You need to have more capabilities than just prompting LLMs. For data science, you need to do more than this, and many of these skills we already covered here. That's what I like about it.
For the first part, I'm very confident. I can deal with RAG systems, embeddings, vector databases, LLM integration and evaluation, and multi-agent systems. I understand practical AI application development.
Lyubomir Ivanov
AI Engineer I wanted to build agents that assist implementation, a small retrieval system for support, and an AI tool to help clients interpret financial data.
The 10-week AI Engineering bootcamp gave me exactly what I needed: practical, hands-on depth without unnecessary theory. Over those weeks, I learned:• How modern AI architectures and frameworks are built and used in real projects
• How to implement LangChain pipelines and work with vector databases
• How to develop platform-independent AI agents
• How to self-host LLMs securely and deploy them in production environmentsThe 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.
Cezar Crintea
Software ArchitectI had a very positive experience with Hyperskill’s AI Engineering Bootcamp. The program is well-structured, practical, and high quality overall.
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.
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.
Gabriel Porras
Senior Full-Stack / Front-End Engineer
Frequently asked questions
$2,499
USD