Break Into AI Testing: The Next-Gen Quality Engineer Skillset!

💻 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.

Yes — at least 3 years of QA experience (manual or automation).

No programming background is needed, though familiarity with testing workflows is helpful.
 

Visit study.university.engenious.io/aicareeraccelerator

Submit your application and confirm your eligibility — only 40 seats per cohort are available. Early applicants receive priority for personalized feedback and project pairing.
 

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.
 

Graduates qualify for emerging QA-AI hybrid roles such as:

✅ AI QA Engineer

✅ LLM Quality Engineer

✅ AI Test Engineer

✅ Evaluation Engineer

✅ AI Red-Teaming Analyst

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 — 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.
 

Absolutely. The WeOptimize project is part of your official coursework and demonstrates real, applied experience testing an AI application.
You can include it under “Projects” or “Experience” on LinkedIn and your resume as:

“Tested and evaluated AI model behavior for WeOptimize — focusing on grounding validation, hallucination detection, and red-teaming strategies.”

This project acts as a verified reference of your AI testing experience, strengthening your professional portfolio.

This program is for QA professionals with 3+ years 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.

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.

Not in this cohort. The January 2026 program focuses exclusively on text-based LLMs, since the current job market is centered on grounding, factuality, and safety validation for text systems.
 

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.
 

It’s a 5-week, hands-on training program designed to help QA engineers transition into AI & LLM Testing roles. You’ll work on a real U.S. startup AI project while mastering model evaluation, red-teaming, and test automation with AI tools.
 

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, currently available only for U.S. applicants.

During checkout, you can select a payment plan through Stripe’s Klarna interface, allowing you to spread tuition into manageable installments.

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
 

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