Infrastructure and Automation Test Engineer

Description

NeuReality's software department is looking for an experienced and highly motivated Infrastructure and Automation Test Engineer to join us and be part of NeuReality’s next-generation, state-of-the-art AI inference server development.

The group designs, develops, validates, and releases inference server and programming SDK software products to make AI deployment easy and cost/power-effective.

Responsibilities:

• QA activities that are required for releasing high-quality products to NeuReality's customers.
• Tight collaboration with architects and development teams.
• Reviewing specifications and technical design documents to provide timely and meaningful feedback.
• Creating product test plans and managing their execution.
• Defining metrics for quality evaluation.
• Design and development of new automatic testing approaches for various features and products developed by NeuReality.
• Consistently reviewing, analyzing, and improving test automation infrastructure and reports.

Requirements

• BSc in Computer Engineering / Computer Science / Electrical Engineering
• 5+ years’ experience in developing automation/validation products with Python as a leading language.
• Experience in infrastructure development (e.g., test environments, test execution frameworks, reporting tools)- Must!
• Experience in validation of complex systems and performance tests.
• Formal and practical knowledge of testing methodologies.
• Hands-on expertise in test writing and automation.
• Excellent knowledge of Linux operating system and good understanding of networking.
• Level of exposure to cloud capabilities, including Kubernetes, virtualization, Docker, etc.
• Proficiency in test automation tools and frameworks (e.g., PyTest, TestNG, JUnit).
• Familiarity with version control systems (e.g., Git) and CI/CD pipelines (e.g., Jenkins, GitHub).

Problem-Solving and Analytical Skills

• Ability to identify, analyze, and debug issues within complex systems, including AI pipelines.
• Knowledge of data validation and techniques to test AI fairness, bias, and performance.
• Communication and Teamwork
• Strong verbal and written communication skills for collaborating with technical and non-technical stakeholders.

Advantages:

• Basic understanding of AI/ML concepts such as training, inference, data preprocessing, and model evaluation.
• Experience testing AI models and ensuring their reliability under diverse data inputs is a plus.
• Knowledge of computer vision, image, or audio processing.
• Experience working in AI-focused companies.
• Experience with testing cloud/data center applications.