Become an AI Evaluation Engineer in 5 weeks

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.

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.
 

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.
 

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.
 

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.

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.
 

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

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.

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

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

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