Quality Engineering
AI Career progression
Select a career track to master specialized quality gates, orchestrate agentic teams, evaluate LLM outputs, and safeguard production environments in the age of AI.
Agentic QE Orchestrator
Learn to act as a manager of AI subordinates, directing workspace control and local validation loops.
Transition from manual QA clicks to orchestrating AI developer and runner agents. Focuses on Git-level workspace isolation, BDD decomposition prompting, state context caching, and autonomous repair loops.
Data Quality & AI/LLM Testing
Audit prompts, validate retrieval accuracy, design evaluation judges, and safety gates.
Master model evaluation and LLM ops. Focuses on testing prompt determinism, implementing LLM-as-a-Judge evaluations, measuring RAG accuracy (context precision and recall), prompt injections, and vector search audits.
AI-Driven Performance & FinOps
Leverage AI agents to analyze distributed tracing and tune cloud infrastructure cost profiles.
Use agents to monitor and automate system scaling and resource allocations. Focuses on load simulation scripts, analyzing telemetry metrics (Jaeger/Prometheus), auto-tuning database settings, and cloud cost FinOps.
AI-Assisted Security & Compliance
Audit dependencies, dynamic vulnerability scanners, and automated remediation gates.
Secure pipelines against supply-chain attacks and code vulnerabilities. Focuses on orchestrating static analysis, dynamic pentesting, auditing regulatory compliance (SOC2/GDPR), and auto-submitting pull requests for security fixes.