Turn Your Team into Agentic AI Engineers
in 10 Weeks
Train 5+ engineers for the cost of 1 senior AI hireFor engineering teams expanding their skills to deliver AI features in production
Get a quote for training for your team
Oops! Something went wrong while submitting the form.
Trusted by engineering teams at:
Case study
JetBrains TeamCity team: 7 production-ready prototypes in 4 weeks
A TeamCity engineering team went through Hyperskill training and built 10 AI prototypes on their own product. 7 of them turned out production-ready, and AI adoption across the team grew measurably.
Read the case study10
AI prototypes built
7
production-ready
4
weeks of training
1
team, their own product
AI solutions examples: production use cases
Built by practitioners,
backed by JetBrains
Powered by
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.
When designing this bootcamp, we kept asking ourselves what developers need to thrive in AI today and build confidence and momentum through real projects.
.png)
I enjoy bridging the gap between complex technology and clear understanding. At Hyperskill Training, I help make advanced concepts accessible to learners.
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. I'm happy to share my learnings with developers seeking innovative position on the market.
With 10+ years blending Software Engineering and UX Design skills and knowledge, I have experience of delivering user-centred products end-to-end. I leverage this integrated expertise to architect and build reliable and valuable AI Agents and LLM-powered apps.
and 10+ experienced educators specializing in technical training for development teams
Hiring vs. Hyperskill Training
Training 5 software engineers costs ~$7,500-10,000.
Hiring 2 AI engineers costs ~$160K/year + 3-6 months recruiting.
Your team ships AI features in 3 months vs. 9+ months.
Hiring 2 AI engineers costs ~$160K/year + 3-6 months recruiting.
Your team ships AI features in 3 months vs. 9+ months.
Capability
Hiring Al Engineers
Hyperskill Training
Time to productivity
~6 months including hiring & onboarding cycle
3 months
Cost per engineer
$80K/year
$60K/year + ~$1,500 one-time
Cultural fit
Uncertain
Perfect (your team)
Knowledge retention
Risk of attrition
100% internal
Scalability
Slow hiring
Train team simultaneously + train new cohorts
Outcomes that matter for your team
Your Team Before
Scattered experiments, no shared framework
Prototypes slowly reaching production
Security and cost concerns blocking deployment
Hard to measure quality or business impact
Chance to build that one AI feature that will boost your core metrics is low
Your Team After
Standardized framework that help you ship features 40%+ faster
Deploy to production speed decreases twice
Security-first, cost-aware architecture
Evaluate results and with clear metrics dashboard
Build meaningful AI solutions that are aimed at business growth
How your teammates upskill
Before: Software Engineer
- Expert in systems, APIs, databases
- Limited exposure to ML/AI
- Ready to expand skillset
During Hyperskill Training
- Building production AI systems
- Learning with expert guidance
- Shipping measurable projects
After: AI Engineer with Product Mindset
- Ships end-to-end AI features
- Thinks in business metrics
- Mentors team on AI best practices
Curriculum that covers end-to-end AI systems lifecycle
Every project connects to a real business case, every module ships a measurable outcome
2 weeks
Module 1: LLMs & Pipelines
Business case: Automate documentation generation
What you'll build:
LLM-powered code documentation system using LangChain
Key skills:
- Prompt engineering
- Pipeline design
- API integration
Evaluation focus:
Output quality
Latency
Cost per request
2 weeks
Module 2: AI Agents
Business case: Automate code review workflows
What you'll build:
Autonomous agent that reviews pull requests and suggests improvements
Key skills:
- Agent architecture
- Tool use
- Reasoning loops
Evaluation focus:
Accuracy
Task completion
Token efficiency
2 weeks
Module 3: Context Engineering
Business case: Scale customer support without headcount
What you'll build:
Context-aware support agent with knowledge retrieval
Key skills:
- RAG patterns
- Vector databases
- Context windowing
Evaluation focus:
Retrieval precision
Response relevance
Hallucination rate
1 week
Module 4: Design Patterns
Business case: Accelerate QA and testing cycles
What you'll build:
Multi-agent testing framework with evaluation chains
Key skills:
- Agent orchestration
- Pattern libraries
- System design
Evaluation focus:
Test coverage
Detection rate
System reliability
2 weeks
Module 5: Security & Privacy
Business case: Deploy AI without data leakage risk
What you'll build:
Secure AI pipeline with PII detection and filtering
Key skills:
- Data sanitization
- Access control
- Compliance frameworks
Evaluation focus:
Data protection
Compliance score
Vulnerability detection
1 week
Module 6: Evals & Monitoring
Business case: Maintain AI system performance over time
What you'll build:
Production monitoring dashboard with automated evaluation
Key skills:
- Metrics design
- Observability
- Performance tracking
Evaluation focus:
System uptime
Model drift
Business KPIs
Leave your contact details to download full curriculum
Oops! Something went wrong while submitting the form.
For the manager
How it runs, from the manager's seat
1. Kick-off call
We agree on goals, your stack and the use cases your engineers will build on. Group size is 4 to 20.
2. Six sprints in 10 weeks
8 to 10 hours per engineer per week. Live sessions are recorded, coursework fits around work within each sprint.
3. Skills measured, not assumed
Assessment before and after the program, progress tracking in between, and a growth report for you at the end.
4. Code review on every project
Instructors review each sprint project and hold open hours for questions, with summary notes after each session.
5. Demo Day
The team presents working AI features to you and the stakeholders you invite.
6. No lock-in
Full refund within the first week. After that, pro-rated refunds case by case.
Your stack, your data
Built on your stack, around your use cases
Tools your team already knows
Python, LangChain, vector databases, the popular LLM APIs, GitHub and Jupyter. Everything is industry standard and production-ready, nothing proprietary to unlearn later.
Projects on your use cases
Every module ships a project tied to a business case: documentation, code review, support, QA, security, monitoring. In a private cohort the cases come from your backlog.
Adjusted to the team
Shorter modules and an adaptable pace for any group. Fully customized cohorts for larger teams, with the curriculum rebuilt around your stack and timeline.
How each sprint works
Learn
Read materials & work in live Q&A sessions
Build
Complete practical project
Review
Get mentor feedback and reiterate
Ship
Deploy & measure results
What your team gains
Internal AI Engineering Playbook
Your team develops a shared framework for evaluating, building, and deploying AI features reducing decision making time and accelerating time-to-production.
Metrics-Driven Culture
Engineers learn to evaluate quality, cost, and performance at every stage, gaining accountability and continuous improvement mindset.
Production-Ready Skillset
From local deployment to monitoring in production, your team gains the full stack, not just the technical parts.
Cost-Aware Architecture
Learn how to optimize for efficiency: when to use local models, how to reduce API costs, and how to scale responsibly.
Security-First Mindset
Build AI systems that meet enterprise security standards from day one.
Faster Feature Velocity
Ship AI-powered features 30-40% faster by eliminating the learning curve and avoiding common pitfalls.
Pricing and terms
One contract for the whole team
$1,500 to $2,000
per engineer
Depends on group size. Training 5 engineers costs about $7,500 to $10,000, less than one month of a senior AI hire.
4 to 20
engineers per cohort
One contract, one invoice. The start date is agreed at the kick-off call, no waiting for a public cohort.
Week 1
full refund window
If the program is not a fit, you get the money back within the first week. Pro-rated refunds after that, case by case.
Get a quoteOr use the ROI calculator below to estimate the return for your team.
Calculate your ROI
Input parameters
Group size
9 people
Expected Annual Impact*
Training duration
10 weeks (≈ 2.5 months)
Key Metrics
Gross Annual Salary (SE)**
$26,460
Gross Annual Salary (AIE)**
$26,460
Program cost***
$26,460
Benefit (Year 1)
$1,543,500
ROI (Year 1)
5733.3%
Payback period
3.2 months
Hiring Savings
+$1,517,040
Net return (Year 1)
+$1,517,040
*Based on 10-30% proven efficiency gain after AI-training
**Based on median Software Engineer (SE) and AI Engineer (AIE) salaries in US & EU (2025)
***Individual conditions apply. Contact us to get your pricing option
**Based on median Software Engineer (SE) and AI Engineer (AIE) salaries in US & EU (2025)
***Individual conditions apply. Contact us to get your pricing option
9 go-to steps of your training
1
Select Your Team
Identify 3-10 software engineers ready to expand into AI Engineers
2
Book 30-minute Discovery Call
Discuss your team's goals, current AI initiatives, and ideal outcomes
3
Receive Custom Proposal
We'll agree on cohort size, timeline, and any curriculum customizations
4
Get Your Team's Knowledge Assessed
Your team will get entry assessment before the training starts
5
Kick Off Training
12-week program begins with team assessment and goal-setting
6
Ship Your First AI Features
By week 8-10, your engineers are able to deploy their first production AI systems
7
Get Your Team's Growth Measured
Based on your team performance, we measure the growth of your employees to identify your new core competencies
8
See Your Impact Reflected in Business Metrics
Notice measurable improvements in delivery velocity, innovation rate, and product quality
9
Scale Your Capability
Train additional cohorts to increase the AI Engineering skillset of your company and drive ROI
Why Hyperskill Training works
Direct access to instructors
Instructors who've built real products and explain complex concepts clearly will help you grow fast. You will have a direct access to instructors during training, plus 1 week after bootcamp.
Structured curriculum
Your team will have:
- 6 practical sprints,
- 10 weeks,
- 8-10 hours studies per week.
Each sprint you'll focus on a project that teaches applied AI development step by step.
- 6 practical sprints,
- 10 weeks,
- 8-10 hours studies per week.
Each sprint you'll focus on a project that teaches applied AI development step by step.
Built around industry needs
Curriculum adapted to innovative market goals and up-to-date AI tools that were thoroughly tested by AI Engineers.
Measure skill growth with data
Receive assessment before and after training, plus continuous progress tracking. You'll get a detailed growth report showing measurable skill improvements across training modules.
Live sessions from industry experts
Participate in workshops with Q&A and feedback to build more efficiently and effectively.
Real business projects
Build AI-focused projects that reflect business tasks. Built on the same principles as programs at MIT or Y Combinator School: you build a product every week.
Common questions
>
Who is this for?
Engineering teams with Python/programming background who want to build AI features. No ML experience required. Ideal for teams of 5-20 developers who need to ship AI capabilities without hiring a dedicated ML team.
>
How flexible is the schedule?
The bootcamp is designed for working engineers with 8-10 hours per week commitment. Live sessions are recorded, and you can complete coursework on your own schedule within each 2-week sprint.
>
What tech stack is used?
We focus on modern AI tools including Python, LangChain, vector databases, and popular LLM APIs. You'll also use GitHub, Jupyter notebooks, and deployment platforms. All tools are industry-standard and production-ready.
>
How is success measured?
Each module includes measurable outcomes: code quality metrics, deployment success, cost optimization benchmarks, and business impact KPIs. Your team will ship real AI features that are evaluated on production readiness.
>
Can we customize for our company?
We offer elements of customization like choosing shorter modules and adaptable pace. Fully customized cohorts are available for larger teams. Contact us for enterprise options.
>
What if my team has mixed skill levels?
The program is designed for varying skill levels but prerequisites are programming knowledge (preferrably Python, 2-8 years working experience). Advanced developers will benefit from production best practices, while beginners get comprehensive foundations.
>
What's the refund policy?
Full refund available within the first 1 week if you're not satisfied. After that, we offer pro-rated refunds on a case-by-case basis. Our goal is your team's success, not lock-in.
Looking for an individual bootcamp?
Join the open 10-week cohort for developers: fixed start dates, pay per seat, your own projects.
Build your internal AI engineering capability with Hyperskill Training
Get a quote for training adapted to your team's goals
Get a quote