Red hat Principal Quality Engineer AI Catalyst Platform Team - Engineering 

Czechia, Southeast, Brno 

407640207

Job Summary

You’ll help accelerate the development of AI prototypes by ensuring seamless platform integration, CI/CD pipelines, and other critical infrastructure to enable high-speed experimentation and iteration.

This role combines technical depth with systems thinking. You’ll lead quality strategy and hands-on validation for AI-powered products, working closely with engineering, product, and AI research to ensure that what we ship is both breakthrough and bulletproof.

What you’ll do

• Champion DevOps, CI/CD, and security practices to streamline the deployment of prototypes and AI features, enabling rapid iteration and testing.

• Translate fast-moving prototypes into testable, reliable features using AI-enhanced validation tools and frameworks

• Mentor senior QA and automation engineers, shaping a high-ownership, high-velocity quality culture within the productization team

• Partner with engineers and AI scientists to define test hooks, observability layers, and validation paths during development—not just after

• Create feedback loops that turn AI-driven insights (logs, usage data, heuristic signals) into continuous quality improvements

What you’ll bring

• 10+ years of experience in software or quality engineering, with deep expertise in building and maintaining production-grade test systems

• Familiarity with modern infrastructure and deployment stacks (e.g., Kubernetes, CI/CD, cloud services)

• Experience designing and running end-to-end test suites that mirror real-world customer behaviors and workflows

• Exceptional collaboration and communication skills—able to align product, engineering, and research around pragmatic quality tradeoffs

• Passion for AI product reliability and ensuring breakthrough features don’t break the user experience

Nice to have

• Experience with AI-enhanced QA tools (e.g., GPT-based test generation, anomaly detection, log synthesis)

• Background in usability engineering or customer workflow validation

• Contributions to internal observability tooling, feedback ingestion systems, or AI regression monitoring frameworks

• Exposure to AI research-to-productization pipelines, including real-time and batch model integrations