Become an AI Evaluation Engineer in 5 weeks
Live (Zoom) • Intermediate • 💎 10000
Part of AI Career Accelerator
Become an AI Evaluation Engineer in 5 weeks
The only hands-on, project-based bootcamp that teaches you to test, measure, and improve AI systems — the skill every AI team is desperate to find.
Duration
5 Weeks
Prerequisites
1+ yrs experience in QA
Background
Good for Noncoders
Format
Live, Hands-On Training
Upcoming
Cohort August 2026
(only for United States)Course Schedule (PDT)
August 1
Saturday
10:00 AM - 2:00 PM
August 2
Sunday
10:00 AM - 2:00 PM
August 8
Saturday
10:00 AM - 2:00 PM
August 9
Sunday
10:00 AM - 2:00 PM
August 15
Saturday
10:00 AM - 2:00 PM
August 16
Sunday
10:00 AM - 2:00 PM
August 22
Saturday
10:00 AM - 2:00 PM
August 23
Sunday
10:00 AM - 2:00 PM
August 29
Saturday
10:00 AM - 2:00 PM
August 30
Sunday
10:00 AM - 2:00 PM
You build real evaluation suites from week one.
LLM Behavior Testing
Prompt injection, jailbreaks, hallucination detection, context window limits
Evaluation Frameworks
Build automated evals with Promptfoo, custom metrics, and assertion suites
Bias & Fairness Auditing
Identify and document model bias across demographics and edge cases
Safety & Red-teaming
Adversarial testing, compliance checks, and responsible AI validation
Is this program ?
- QA Engineers and SDETs who want to stay relevant as AI reshapes software testing
- Automation Engineers looking to expand beyond traditional frameworks into AI system testing
- Manual Testers without any programming background, ready to learn from scratch, hands-on.
- Test Leads and QA Managers responsible for quality, risk, and governance in AI-powered products
- Software Engineers exploring AI-adjacent roles such as prompt engineering or AI quality
“I know how to test APIs and UIs… but AI apps feel different.”
→ This path bridges that gap.
Be Fully AI Job-Ready by Graduation
Career readiness isn't an afterthought — it's part of the program. You'll get dedicated coaching, a strategy to grow your LinkedIn presence, and real project experience you can speak to in any interview.
LinkedIn
PortfolioGet mentorship, job opportunities and peer support throughout Discord community, plus a network that stays with you.
Job leads
CommunityAI isn't replacing you. It's your next career move.
Why learning is a must for Every QA in 2026?
AI Evaluation Engineering is already a specific, high-demand skill
More than 3,000+ job openings across the US
Top AI Evaluation Engineers earn over $300K/year
AI Testing Skills are in-demand in every company
You After the Course
AI Evaluation Engineer
Portfolio-ready AI and LLM testing experience built on a real U.S. startup project with live, hands-on training.
$200,000
Expected salary
Skills
Tools
Promptfoo
LM Studio
Agenta
Arato.ai
OpenAI API
Anthropic APIProof of Work
Break Into AI Testing: AI & LLM Testing Bootcamp
EnGenious University
AI Application Testing Portfolio
Hands-on artifacts covering LLM evaluation, prompt injection and jailbreak testing, multi-model comparison, and hallucination detection — built using Promptfoo, OpenAI API, Anthropic API, and LM Studio.
Will I get a ?
Of course! It'll look great on your resume and LinkedIn
Your Name
Break Into AI Testing: AI & LLM Testing Bootcamp
Instructors:
Tagir Fakhriev, Igor Dorovskikh, Amanda Curtis, Gregory Goldshteyn
Finished: August 30, 2026
Number of lectures: 10 / Total hours: 40
university.engenious.io
university@engenious.ioOur alumni at
Hershal Walton
Gen AI Product Manager
“This course puts you in a leading frontier for new opportunities that should be coming up very soon”
Max Volvich
QA Engineering Manager @ Sirona Medical
“After this course, I not only understand how AI systems work behind the scenes, but I also feel confident leading teams building and testing them.”
Mavis Herring
AI Quality Engineer @ WeOptimize AI
“This course built confidence. As soon as I posted that I finished the course on LinkedIn, many recruiters started approaching me.”
Our alumni at
We've taught to ...
Mona Pokharel
No review text provided
Abilash Chinatalacheruvula
Excellent course content with real hands-on exercises and practices.
Vinutha Lingaraju
I recently completed the AI course at Engenious University and overall it was a good learning experience. As someone working in the QA and test automation space and looking to expand into AI, I found the course a worthwhile step in that direction. The hands-on projects were definitely the strongest part of the programme. Being able to work through real problems rather than just absorbing theory made the concepts stick, and I came away with practical skills I can apply directly in my work.
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Richard Cottrell
This 5-week course is an absolute masterclass for anyone transitioning from traditional QA into the world of AI and LLMs. It moves far beyond the hype to tackle the genuine technical challenges of testing non-deterministic solutions, providing a robust framework for building a real-world test strategy. What made this course stand out: 1. Deep-Dive Tooling: We got hands-on experience with Promptfoo and LM Studio, learning exactly how to leverage them for measurable results and why. 2. Red Teaming & Strategy: Moving from theory to the actual execution of Red Teaming was a highlight, as was the focus on the financial cost of different using different LLMs and testing approaches. 3. Practical 'Learn-See-Do' Format: The structure was excellent. Every session followed a clear 'concept demonstration group exercise' flow. Working through these concepts in breakout groups ensured the knowledge actually stuck. 4. Total Support: The flexibility of having recorded sessions was a lifesaver for busy weeks and having the tutors available via Discord throughout the 5 weeks meant no question went unanswered. If you want to understand how to actually validate AI performance and manage the risks of non-deterministic systems, this is the course you need. Highly recommended!
Show More
Gokulnath Kumar
No review text provided
Mona Pokharel
No review text provided
Abilash Chinatalacheruvula
Excellent course content with real hands-on exercises and practices.
Vinutha Lingaraju
I recently completed the AI course at Engenious University and overall it was a good learning experience. As someone working in the QA and test automation space and looking to expand into AI, I found the course a worthwhile step in that direction. The hands-on projects were definitely the strongest part of the programme. Being able to work through real problems rather than just absorbing theory made the concepts stick, and I came away with practical skills I can apply directly in my work.
Show More
Richard Cottrell
This 5-week course is an absolute masterclass for anyone transitioning from traditional QA into the world of AI and LLMs. It moves far beyond the hype to tackle the genuine technical challenges of testing non-deterministic solutions, providing a robust framework for building a real-world test strategy. What made this course stand out: 1. Deep-Dive Tooling: We got hands-on experience with Promptfoo and LM Studio, learning exactly how to leverage them for measurable results and why. 2. Red Teaming & Strategy: Moving from theory to the actual execution of Red Teaming was a highlight, as was the focus on the financial cost of different using different LLMs and testing approaches. 3. Practical 'Learn-See-Do' Format: The structure was excellent. Every session followed a clear 'concept demonstration group exercise' flow. Working through these concepts in breakout groups ensured the knowledge actually stuck. 4. Total Support: The flexibility of having recorded sessions was a lifesaver for busy weeks and having the tutors available via Discord throughout the 5 weeks meant no question went unanswered. If you want to understand how to actually validate AI performance and manage the risks of non-deterministic systems, this is the course you need. Highly recommended!
Show More
Gokulnath Kumar
No review text provided
Mona Pokharel
No review text provided
Abilash Chinatalacheruvula
Excellent course content with real hands-on exercises and practices.
Vinutha Lingaraju
I recently completed the AI course at Engenious University and overall it was a good learning experience. As someone working in the QA and test automation space and looking to expand into AI, I found the course a worthwhile step in that direction. The hands-on projects were definitely the strongest part of the programme. Being able to work through real problems rather than just absorbing theory made the concepts stick, and I came away with practical skills I can apply directly in my work.
Show More
Richard Cottrell
This 5-week course is an absolute masterclass for anyone transitioning from traditional QA into the world of AI and LLMs. It moves far beyond the hype to tackle the genuine technical challenges of testing non-deterministic solutions, providing a robust framework for building a real-world test strategy. What made this course stand out: 1. Deep-Dive Tooling: We got hands-on experience with Promptfoo and LM Studio, learning exactly how to leverage them for measurable results and why. 2. Red Teaming & Strategy: Moving from theory to the actual execution of Red Teaming was a highlight, as was the focus on the financial cost of different using different LLMs and testing approaches. 3. Practical 'Learn-See-Do' Format: The structure was excellent. Every session followed a clear 'concept demonstration group exercise' flow. Working through these concepts in breakout groups ensured the knowledge actually stuck. 4. Total Support: The flexibility of having recorded sessions was a lifesaver for busy weeks and having the tutors available via Discord throughout the 5 weeks meant no question went unanswered. If you want to understand how to actually validate AI performance and manage the risks of non-deterministic systems, this is the course you need. Highly recommended!
Show More
Gokulnath Kumar
No review text provided
Learn From
CEO and Founder
Igor is an accomplished CEO and Founder of Engenious.io, with 15+ years of experience in software testing and development and over a decade in management. He has worked at Barnes & Noble, Expedia, Tinder, and consulted at Apple and Grammarly. In the mentorship program, Igor offers expertise in building a testing process from scratch, leadership success, understanding C-level executives' expectations, selecting the right technology stack, providing and collecting feedback, and team growth. Mentees benefit from Igor's insights on creating efficient testing processes, fostering productive teams, aligning with executive priorities, making informed technology choices, establishing feedback channels, and securing resources for team development. With Igor as their mentor, participants gain valuable knowledge, skills, and perspectives to excel as Dev/QA Directors or Managers.
Quality Engineering Manager
Seasoned IT professional with 14+ years of experience in Software Engineering, Quality Assurance, and Automation. Skilled in leading teams, designing test strategies, and building automation frameworks across diverse industries. Adept at leveraging modern tools, AI-driven testing approaches, and cloud technologies to deliver high-quality, scalable solutions. Holds a Bachelor’s in Management Information Systems and a Master’s in Information Technology with proven success supporting enterprise-level clients and Fortune 500 companies.
Founder of Lemonade Tech & QA Manager
Amanda Curtis is a QA leader and founder of Lemonade Tech, with a passion for responsible AI adoption and helping teams cut through tech overwhelm. With 10+ years experience leading QA teams and modernizing testing practices, Amanda focuses on practical solutions that improve software quality while keeping technology approachable and human-centered. Helping organizations “find the good in tech” by cutting through complexity and focusing on what truly adds value.
Software Engineer
10 years of experience in the tech industry; Senior Android Engineer in Platform team. Expert in CI/CD pipelines, test automation, and mobile infrastructure; passionate about developer productivity and workflow optimization.
Instructor AI Accelerator
Visionary QA Leader with substantial experience in the IT industry. Worked across Salesforce, Sony, and now as part of Video Engineering and Quality Assurance, he leads the strategy for high-concurrency streaming environments, where a single second of latency is unacceptable.
Instructor AI Accelerator
Software development and QA experience for over 20 years. Alex has worked at well-known companies such as Oracle and HCL Software, and has strong expertise in functional, regression, and automated testing, complemented by a background in Java-based application development. Skilled in WebdriverIO, Selenium, JavaScript, and CI/CD pipelines, with hands-on experience in building and supporting enterprise applications.
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Senior iOS Engineer to Co-Founder & CTO·WeOptimize.ai
Vladimir is an experienced engineer with 8+ years in iOS/macOS development, specializing in AI-powered solutions. As the Co-Founder & CTO of WeOptimize.ai, he leverages AI to optimize workflows and enhance productivity. He has a track record of delivering innovative products for both startups and large enterprises.
Instructor AI Accelerator
Quality Engineering leader driving scalable automation and delivery across enterprise SaaS and AI/LLM systems. Leads global QA teams and embeds quality into revenue-critical release pipelines, strengthening reliability and trust in AI-driven products.
What you'll in 5-weeks in 5-weeks
01
Week
Day 1: AI Fundamentals and Tool Setup
Introduction: Course rules, setting up the permanent Discord community channel. Theory: LLM basics, transformer architecture, differences between traditional software testing and AI system testing. Hands-on: Environment setup (local and cloud models). Initial interactive model exploration comparing local (LM Studio) and commercial (OpenAI, Anthropic) models using the same prompt. Vulnerabilities: Introduction to the seven unique AI testing challenges (e.g., security, hallucination, bias). Homework: Complete environment setup and design 3+ test prompts targeting the vulnerabilities.
Day 2: PromptFoo Basics
Tool Introduction: Learning PromptFoo for systematic AI testing. Shift to hands-on, Q&A, and group exercises (minimal presentations). Configuration: Overview of PromptFoo's configuration (YAML structure), including providers (LLMs), prompts, and initial deterministic assertions (pass/fail checks). Practice utilizing variables within prompts. Integration: Testing commercial and local models via PromptFoo. Homework: Practice building test suites and reviewing assertions documentation.
02
Week
Day 3: Prompt Engineering and Cost Evaluation
Prompt Engineering: Defining the rules and constraints of the system (System Prompt) and crafting effective test inputs (User Prompt). Using AI (LLMs) to generate effective test prompts. Evaluation: Hands-on workshop on LLM cost evaluation (budgeting) by running prompts against multiple models to compare cost per request. Organization: Structuring the testing framework using file-based prompt configurations.
Day 4: Advanced Assertion & Career Prep
Advanced Testing: Deep dive into assertions, particularly Model-Graded Assertions (MGA), where an LLM acts as a judge (LLM Rubric) to evaluate output quality (relevancy, factuality). Testing using CSV-based files for structured test data. Career Start: Introduction to LinkedIn Personal Branding; documenting early achievements and incorporating AI testing keywords (e.g., prompt engineering, LLM testing) to profiles. Homework: Review PromptFoo Red Teaming documentation.
03
Week
Day 5: Red Teaming Concepts
Equator Point: Course halfway review. Red Teaming: Defining red teaming as simulating adversarial inputs (like a comprehensive baseline report) to find vulnerabilities (e.g., security, bias). Discussion of vulnerability frameworks like the OWASP Top 10 for AI. Strategy: Understanding Red Teaming workflow (defining strategy, execution, analysis) and configurations. Comparison of red teaming types (Small, Large, XXL/Extensive).
Day 6: Testing a Real Application (Red Team Web App)
Application Architecture: Reviewing the high-level architecture of the application (Orchestrator, Guard LLM, specialized LLMs, knowledge bases like Jira/Confluence/Figma). Testing Mode: Focusing on end-to-end black box exploratory testing via the application's chat interface. Using the provided "Source of Truth" as acceptance criteria. Bug Reporting: Hands-on exercise reporting and documenting bugs, including reproduction steps and linking them to specific AI vulnerabilities.
04
Week
Day 7: Red Teaming Execution & Triage
Application Setup: Finalizing the Red Teaming configuration by inputting a comprehensive application context (main purpose, features, system rules) into PromptFoo. Group Triage: Teams exchange reported bugs and attempt to reproduce and validate issues found by classmates. Advanced Testing: Hands-on session applying PromptFoo for complex scenarios, including multi-turn conversation testing using JSON objects for regression. Tool Exposure: Alternative testing tool.
Day 8: Tool Exploration & Post-Launch Monitoring
Post-Launch Tools: Demo and discussion of tools used for monitoring and maintaining LLM models pre- and post-launch (e.g., Arato wrapper). New Tool Assignment: Introduction to Agenta (a comparable, alternative LLM testing platform). Homework: Explore and evaluate Agenta to apply foundational testing concepts learned from PromptFoo. Research other AI testing tools in the market (consulting mindset).
05
Week
Day 9: Agenta Review & LinkedIn Strategy
Tool Comparison: Reviewing homework findings on Agenta, applying foundational concepts (Evals, variables) learned from PromptFoo to a new platform. Career Branding: Strategies for content creation and influence building on LinkedIn. Using generative AI tools (e.g., Claude, ChatGPT) as brainstorming partners for posts, while avoiding generic copy-paste content. Accomplishments: Workshop focused on drafting AI LLM testing accomplishment statements for resumes/profiles, quantifying the business impact of skills learned. Homework: Post tailored AI testing content on LinkedIn and engage (comment/repost) with classmates' posts.
Day 10: Final Optimization & Interview Prep
Final Profile Optimization: Updating LinkedIn profiles and resumes with core AI testing skills (prompt engineering, red teaming, hallucination detection, token consumption). Interview Preparation: Review of common AI testing interview questions (e.g., scaling tests, verifying factual responses, token consumption, security, testing LLMs with other LLMs). Wrap-up: Final remarks, community engagement commitment, and discussion of post-course resources.
You Won't Find Anywhere Else
Lifetime Community Access
Join our Discord community with 1000+ QA professionals.
Ongoing support from instructors and alumni.
Regular follow-up sessions and career guidance.
Recorded Sessions
All sessions recorded and can be accessed up to for 1 year.
Review program materials and session recording anytime.
Never miss important concepts.
What's ? Even More
Internship in AI Startup (Stella Foster)
Take your skills further with a hands-on internship designed to give real-world experience working with production AI systems, testing frameworks, and voice/SMS automation tools.
Advanced RAG & Multi-Agent Testing
Go deep into Retrieval-Augmented Generation, vector databases, grounding, multi-agent workflows, tool usage, and complex evaluation frameworks used in modern AI systems.
Minimum system
macOS:
Processor: Apple Silicon M1, M2, M3 or M4
Memory: 16 GB RAM (or higher)
Storage: 30 GB free SSD space
Note: Mac OS systems without an M chip are not supported
Windows:
Processor: Intel Core i5 / i7 or AMD Ryzen 5 / 7
Memory: 16 GB RAM (or higher)
GPU: Dedicated GPU with ≥ 6 GB VRAM (e.g., NVIDIA RTX 2060 / 3060)
Storage: 30 GB free SSD space
1. Project-based learning: You test a real U.S. AI startup product
2. 95% hands-on: Minimal theory, maximum practice
3. Mentor-led live sessions (with recordings for 1-year access)
4. Career coaching and interview prep built into the final module.
Yes — at least 1 year of QA experience (manual or automation).
No programming background is needed, though familiarity with testing workflows is helpful.
Week 1: AI fundamentals, environment setup, and AI-assisted testing basics
Week 2: LLM testing, debugging model failures, and assertion strategies
Week 3: Advanced red-teaming, grounding validation, and safety testing
Week 4: Open-source tools, workflow automation, and model evaluation frameworks
Week 5: Resume optimization, job prep, and final capstone showcase
Submit your application and confirm your eligibility — only 50 seats per cohort are available. Early applicants receive priority for personalized feedback and project pairing.
Not sure if the program is right for you? Book a free AI Career Strategy call before you enroll.
Yes, currently available for U.S. and Canada applicants. May be available for other countries - only for U.S. Dollars payments.
During checkout, you can select a payment plan through Stripe’s Klarna interface, allowing you to spread tuition into manageable installments.
The training is a 5-week long training. It includes 10 lectures (40 hours). Classes are held on weekends, Saturdays and Sundays from 10.00 am to 2.00 pm PST
Yes — we provide comprehensive career preparation and mentorship support, though employment is not guaranteed.
During the final week of the cohort, we dedicate 4 hours to focused career development sessions covering:
- LinkedIn optimization and personal branding
- Job search strategies tailored to AI and QA markets
- Resume updates and portfolio positioning for AI Testing roles
For top-performing graduates, Engenious may offer short-term contract roles through partner projects or internal initiatives. However, timelines and availability are not guaranteed.
After graduation, you can continue growing through our Mentorship Program — designed to help you refine your AI QA skills, gain real-world experience, and stay connected with the Engenious professional network.
💻 Windows
✅ Windows 10 (64-bit) or newer
✅ Intel i5 (8th Gen +) / AMD Ryzen 5 +
✅ 8 GB RAM (min), 16 GB recommended
✅ 20 GB free storage
✅ Node.js v18+, Python 3.8+, VS Code, Git (Docker optional)
✅ Chrome or Edge browser
✅ Stable 10 Mbps+ internet + webcam
🍏 macOS: macOS Monterey (12+) or newer
✅ Apple M1/M2 chip or Intel i5 (2018 +)
✅ 8 GB RAM (min), 16 GB recommended
✅ 20 GB free storage
✅ Homebrew, Node.js v18+, Python 3.8+, Docker (optional)
✅ Chrome or Safari browser
✅ Reliable 10 Mbps+ connection + webcam
💡 Tip: Dual-monitor setups improve productivity for labs and evaluations.
You’ll explore multi-agent orchestration concepts by testing a live AI app (WeOptimize).
We emphasize end-to-end testing rather than isolated stages.
You’ll learn to:
✅ Identify failure points in multi-turn interactions
✅ Evaluate guardrail effectiveness and memory behavior
✅ Detect safety leaks and context loss across chained logic
✅ This reflects real QA work in AI product teams — black-box testing of complex reasoning flows.
Yes, but in this course. "Break Into AI Testing" bootcamp focuses exclusively on text-based LLMs, since the current job market is centered on grounding, factuality, and safety validation for text systems.
To all of the alumni as a part of our AI Career Accelerator path we offer a next Advanced course focused on the more kinds of AI.
Yes — these are included through:
1. Drift indicators and re-evaluation cycles
2. Synthetic variation testing
3. Failure pattern analysis
3. Feedback loop triage
You’ll learn to identify regression behaviors and emergent defects as AI systems evolve — essential for real-world QA teams.
You’ll gain practical skills to:
1. Test and validate AI-powered applications and LLMs
2. Detect hallucinations, bias, and factual drift
3. Evaluate grounding and context reliability
4. Use frameworks like Promptfoo and LLM-graded assertions
5. Build a portfolio-ready capstone project aligned with current job roles
This program is for QA professionals with 1+ year of manual QA experience who want to move into the fast-growing world of AI Quality Assurance. No coding or AI experience is required — just curiosity, analytical thinking, and a testing mindset.
These are addressed through:
- Deterministic & weighted assertions
- LLM-graded accuracy evaluation
- Safety, bias, and hallucination detection patterns
- Multi-model comparison
- Context-based grounding checks
Weeks 2–3 focus on advanced Promptfoo assertions and red-team strategies to identify hallucinations, factual drift, and grounding violations.
You won’t build a RAG pipeline from scratch, but you’ll learn how to evaluate retrieval-augmented systems — a core QA responsibility in AI production environments.
Still have questions?
Not sure if this program is right for you? Need help choosing the best path or want to understand the curriculum better
Our AI assistant is here to help — fast, friendly, and available anytime.
Ask AI CAREER ASSISTANT
100% money back guarantee
If you're not satisfied by Week 1, claim a full refund, no questions.
Seats are limited to 40 registrants. Secure your spot today.
Ready to begin your ?
The future of QA isn't about choosing between Selenium or Playwright - it's about Mastering Prompt Engineering, LLM Testing and AI Debugging.