About this role
Testing and validating non-deterministic AI outputs forms the core daily activity, alongside building robust regression suites and evaluation datasets for the data intelligence platform. Engineering teams collaborate closely to design automated testing solutions, support mission-critical processing services, and verify applications across unit, integration, and end-to-end testing layers.
Everpure expands beyond traditional storage to help organizations govern, secure, and activate distributed data for the artificial intelligence era. Operating within the Data Management business unit, the team builds an intelligent platform that turns complex information into trusted, governed assets. Professionals who thrive here enjoy tackling the quality challenges specific to AI systems like drift, hallucination, and guardrail verification.
This role exists to ensure platform security, reliability, and scalability through modern engineering and quality practices. Successful candidates bring software development experience, familiarity with cloud platforms, and a genuine interest in solving the unique hurdles of validating machine learning workflows.
What you will do
- Design and run quality strategies for platform AI-driven features by building evaluation datasets, regression suites, and metrics for non-deterministic outputs.
- Create and maintain test automation for the data intelligence platform across unit, integration, and end-to-end testing layers.
- Collaborate with peers and stakeholders to identify requirements and take solutions from initial design through production.
- Contribute to engineering best practices including code reviews, documentation, continuous integration, and continuous deployment.
- Experiment with new technologies, platform ecosystems, and AI-assisted approaches to improve automation and engineering efficiency.
- Share testing expertise and quality engineering best practices to support continuous improvement across the team.
What you need
- Experience designing, implementing, testing, and operating software applications or platform components.
- Proficiency in Python, Java, Go, or another modern programming language, with an interest in learning new technologies.
- Experience with cloud platforms such as AWS, Azure, or Google Cloud, including Kubernetes.
- Experience working with SQL and NoSQL databases, data structures, algorithms, and modern software engineering practices.
- Strong collaboration, communication, and problem-solving skills, with the ability to work effectively in a team environment.
Nice to have
- Exposure to testing AI or ML-backed features such as evaluation harnesses, prompt and model regression testing, or measuring output quality.
- Familiarity with AI-assisted QA automation and cloud networking concepts.
Benefits
- Flexible time off
- Wellness resources
- Company-sponsored team events
Skills and tech stack
Questions candidates ask
What is the remote work policy for this role?
The team operates primarily as an in-office environment. Employees are expected to work from the Prague office in compliance with company policies, unless on PTO, work travel, or other approved leave.
What kind of technologies will I work with?
The tech stack includes modern programming languages like Python, Java, or Go, alongside cloud platforms such as AWS, Azure, or Google Cloud, Kubernetes, and SQL or NoSQL databases.
What specific AI quality challenges will this position address?
The work focuses on the quality challenges specific to AI systems, which include non-determinism, drift, hallucination, and guardrail verification.