Senior Software Quality Engineer

Do you want to work in a team where learning new things is a daily routine? Do you want to be part of the AI movement that is shaping the IT industry? Become a quality leader in a team that will play a critical role in infusing Red Hat products with generative AI. Our aim is to develop a platform other teams at Red Hat will use to quickly and easily develop virtual assistants for Red Hat products and we seek a quality engineer who will help us make this happen. Be prepared to work in a highly distributed environment which is largely unshaped and very fast paced, adding a bit of startup freshness and agility to our corporate stability. In this role, you will  be responsible for all aspects of quality for the ROAD project, including designing test plans, extending automation frameworks, and developing new automated tests. You will make sure our platform fulfills the highest possible enterprise standards for quality, performance, usability, and supportability. You will gain a deep technical understanding of a variety of popular technologies, including containers, Red Hat OpenShift AI, langchain, vector databases, RAG and others. You will also have an opportunity to contribute to the formation of a broader ecosystem which will give Red Hat customers and the open source community the ability to be self-sufficient in deploying and operating virtual assistants for Red Hat products. In collaboration with the team’s manager and lead engineer, you will become the face of the team and have the opportunity to influence and coordinate the quality standards with partnering teams. This is a very high profile project and the position is critical to the project's future success. It includes a lot of flexibility and therefore requires a highly motivated and self-driven person.

Primary Job Responsibilities

• Work in a fast-paced agile globally distributed environment of talented engineers

• Execute manual and automated tests for the ROAD project and deliver clear status in a timely manner.

• Conduct failure analysis, explore, identify and document new bugs, advocate for the resolution of  bugs and communicate the impact on customers to developers and support;

• Coordinate bug verification, regression testing as well as project delivery related testing with partnering teams

• Design, develop and maintain automation frameworks, CI and scripts supporting ROAD testing

• Participate in the test planning and product planning processes

• Conduct new feature research and design test cases

• Constantly learn new things and maintain an overview of current technologies

• Provide mentoring and leadership to other team members

• Exercise influence and customer centricity to motivate product teams to adopt the common platform rather than to build their own code from scratch.

Required Skills

• Bachelor's degree or higher, or equivalent in computer science or a related field

• 5+ years of professional experience working in QE or similar roles

• Familiarity with testing methodologies and techniques

• Experience with generative AI, at minimum as advanced user level

• Understanding the elementary concepts of generative AI like prompt tuning, prompt engineering, RAG, tokenization, chunking

• Programming skills in shell, Ruby, Golang, Python or another programming language

• Demonstrated ability to quickly learn new technologies with minimal guidance; willingness to learn and work with new technologies

• Excellent written and verbal communication skills and ability to work with distributed teams.

Preferred skills

• Experience with CI/CD and technologies like Prow, Tekton or ArgoCD

• Experience testing multi-agent architecture or langchain-based code

• Contributions to open source projects or publicly available code samplesDesign, develop and maintain automation frameworks and scripts with Ruby, Golang and Python for OpenShift testing and implementation

The salary range for this position is $101,300.00 - $162,050.00. Actual offer will be based on your qualifications.

Pay Transparency

Red Hat determines compensation based on several factors including but not limited to job location, experience, applicable skills and training, external m