AI Quality Engineering
AI Quality Engineering & QA Orchestration Professional
Build, evaluate, orchestrate and govern quality across AI-powered systems.
4 Progressive Capability Layers
80% Applied Learning
Integrated GitHub Portfolio
8 Weeks
Why It Matters
Traditional QA
Test defined user flows and investigate defects
→
Automation QA
Execute repeatable regression and CI checks
→
API & Integration Testing
Validate services, contracts and data
→
AI Response Testing
Evaluate non-deterministic outputs against quality criteria
→
LLM & RAG Evaluation
Measure grounding, retrieval, relevance and safety
→
Agent Testing
Validate planning, tools, memory, state and stopping conditions
→
AI QA Orchestration
Coordinate the complete quality system across agents, evaluators and humans
LLM Applications
Prompts, outputs, formats and factual quality
RAG Systems
Retrieval, grounding, context and citation quality
AI Agents
Planning, tool use, memory, state, loops and stop conditions
AI Workflows
APIs, data, models, controls and human hand-offs
Hallucination & weak grounding
Unsafe or biased output
Incorrect tool selection
Incorrect tool parameters
Infinite loops
Failed stopping logic
Schema failures
Sensitive-data exposure
Quality drift after prompt changes
Quality drift after model changes
Modern AI systems include LLMs, RAG pipelines, agents, tools, memory, workflows and human hand-offs. Quality engineering must therefore move beyond isolated pass/fail testing into evaluation systems that reason about evidence, uncertainty and risk.
Who Should Attend
QA Engineers moving into AI quality engineering
Automation Engineers and SDETs
Test Architects and Quality Engineering Leads
Engineers validating AI-powered applications
Quality professionals working with LLMs, RAG systems or AI agents
Senior QA professionals preparing for AI QA orchestration capability
Engineers seeking a portfolio-driven applied learning path
Learning Journey
LAYER 1 · WEEK 1
Engineering Foundation
LAYER 2 · WEEKS 2–4
AI Evaluation Engineering
LAYER 3 · WEEKS 5–6
Agent Quality Engineering
LAYER 4 · WEEKS 7–8
QA Orchestration
INTEGRATED CAPSTONE
Integrated AI Quality Orchestration System
Curriculum
What You’ll Achieve
BUILD
Python, Pytest, APIs and reusable quality automation foundations
EVALUATE
Golden datasets, rubrics, calibrated evaluators and LLM quality measurement
VALIDATE RAG
Retrieval quality, grounding, citations and root-cause diagnosis
TEST & SECURE AGENTS
Planning, tools, memory, state, loops, permissions and human controls
ORCHESTRATE
Coordinate specialist QA agents, routing, shared state and evidence aggregation
OPERATE & GOVERN
CI/CD quality gates, production traces, drift detection and human release decisions
Build Python, Pytest, APIs and reusable quality automation foundations. Evaluate with golden datasets, rubrics, calibrated evaluators and LLM quality measurement. Validate RAG retrieval, grounding and citations. Test and secure agents. Orchestrate specialist QA agents. Operate and govern through CI/CD quality gates, production traces, drift detection and human release decisions.
AI Agent Testing Workflow
Goal
→
Plan
→
Act / Tool
→
Observe
→
Stop or Repeat
Agent testing must validate the complete trajectory, not only the final response. Planning, tool selection, parameters, memory, state, stopping conditions and recovery behaviour all contribute to system quality.
Planning validation
Tool selection
Parameter validation
Tool mocks
State & memory
Loop detection
Stop conditions
Security & permissions
Automated Quality Gates Workflow
Code Change
→
Pytest & Evaluation Suite
→
AI Quality Checks
→
Evidence & Reports
→
GitHub Actions
→
Human Release Decision
The program moves quality engineering from local test execution into automated quality gates, production feedback loops and evidence-based release decisions.
Technology Ecosystem
CORE ENGINEERING
Python 3.11 · Pytest · REST APIs · JSON Schema · Git · GitHub
AI & AGENT LAYER
OpenAI API · Mock LLMs · Live LLMs · Tool / Function Calling · LangGraph or equivalent orchestration
EVALUATION LAYER
Deterministic Evaluators · Rubric-Based Evaluation · LLM-as-Judge · Judge Calibration
RAG Metrics · Groundedness · Faithfulness · Optional exposure to RAGAS, DeepEval or LangSmith
DELIVERY & EVIDENCE
GitHub Actions · HTML Reports · JSON Reports
Structured Logs · Execution Traces · Quality Scorecards
AI-ASSISTED DEVELOPMENT
VS Code Coding Agent
Capstone / Portfolio
ai-qa-orchestration/ app/ llm/ rag/ agents/ tools/ orchestrator/ router/ state/ policies/ evidence/ evaluators/ deterministic/ llm_judge/ rag/ safety/ agent/ tests/ api/ llm/ rag/ safety/ agents/ orchestration/ datasets/ prompts/ policies/ reports/ traces/ .github/workflows/ .env.example requirements.txt README.md
Portfolio Evidence
✓
Architecture — Evaluation architecture and risk/coverage matrix
✓
Datasets & Tests — Golden datasets, automated evaluation suite, RAG, safety and agent tests
✓
Orchestration — Multi-agent workflow and security/permission policy
✓
Operations — CI/CD quality gate, traces and release-readiness dossier
✓
Demonstration — Architecture README and final live walkthrough
✓
Agent testing
✓
Loop and stop-condition testing
✓
Automated reports
✓
GitHub Actions workflow
✓
Technical README
Integrated AI Quality Orchestration System: one enterprise quality system for an AI-powered support or knowledge agent using RAG, multiple tools, memory and human escalation. The learner identifies risks, routes test suites, evaluates API, LLM, RAG, safety and agent behavior, consolidates evidence, escalates uncertainty and produces a human-readable release recommendation requiring human approval.
Career Relevance
AI QA Engineer
GenAI Quality Engineer
LLM Validation Engineer
Agent Validation Engineer
AI Quality Automation Engineer
Relevant pathways include AI QA Engineer, LLM Validation Engineer, GenAI Quality Engineer, RAG Evaluation Engineer, Agent Validation Engineer, AI Quality Automation Engineer, Quality Engineering Lead for AI Systems and AI QA Orchestration. AI QA Orchestration is an emerging capability direction rather than a standardised mass-market job title. Career outcomes depend on prior experience, practice depth, communication and the quality of the completed portfolio.
Frequently Asked Questions
Ready to build your AI QA orchestration journey?
Talk to Us