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A leading software company seeks a Senior Software Development Engineer in Test (SDET) to oversee QA processes. The role involves managing testing environments, automating processes, and using AI technologies for QA enhancement. Ideal candidates will have strong expertise in C# programming and experience with automation tools.
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The Senior Product Developer is responsible for overseeing the maintenance and enhancement of the testing infrastructure (automation and manual). Ensure all testing machines comply with security requirements. Manage and support internal toolset for automation infrastructure.
Job Description & Requirements:
Strong ownership of Automation QA process as part of a global product development/release cycle.
Managing complex and distributed QA environments for Automated and Manual Testing (Test Labs).
Coordination and communication with the development team (Business Specialists, Engineers, QAs and DevOps) and IT department.
Maintenance and development of internally developed auto test software tools.
Developing and Implementing Tools and Utilities for optimization of QA Activities.
Incorporating AI-Enabled Product Development Initiatives for Acceleration.
Responsibilities:
Conduct daily monitoring and troubleshooting the QA environments. Ensure the automation infrastructure executes auto-tests and delivers results on the testing portals (within the expected timeframe) to be available for development teams. Perform preliminary analysis of differences and failures of the automation test results. Running the automation test scripts.
Deployment of QA environments (virtual machines). Create and configure virtual machines (VMs) for automated testing of new versions. Establish and configure VMs for manual testing of new versions and patches.
Perform source code analysis to identify and resolve issues, as well as implement necessary improvements to the testing and automation infrastructure. Assistance to QAs in maintenance and development of auto test library (test scripts). Enhance and optimize automation scripts for improved efficiency.
Ensure that all VMs within the testing infrastructure consistently adhere to security standards, including recent updates, malware protection, and accessibility of shared resources.
Carrying out periodical performance and load capacity tests of the product. Maintenance and further development of internal auto test software. Resource and HW planning.
Coordination with and support of the development team (Business Specialists, Engineers, QAs and DevOps). Following requirements of the existing methodology and architecture standards.
Provide mentorship to other team members. Participate in cross-functional tasks and other responsibilities as necessary.
Design, develop, and maintain automated tools and utilities to enhance the efficiency and effectiveness of the QA process. Continuously assess and improve QA workflows by creating custom utilities to address specific challenges in the development cycle.
Leverage artificial intelligence and machine learning technologies to automate repetitive tasks, improving productivity across development and testing. Driving the integration of AI-driven testing tools to predict potential issues, enhance test coverage, and improve testing accuracy.
Skills/Technologies (Required):
Windows Administration, VMWare vSphere
MS SQL Server, MongoDB
IIS
Skills/Technologies (Preferred Skills):
Programming Languages: VBScript
Test Automation: TestComplete
CI/CD: Jenkins, Docker
Version Control: Git
Project Management Tools: Jira, Confluence
Web technologies: HTML, CSS, XML, Xpath
AI in Testing / Development Tools: GitHub Copilot, ChatGPT APIs / OpenAI APIs
What you need to succeed:
Strong expertise in Windows administration.
Strong (3+ years) experience in C# programming.
Experience in designing and implementing test automation frameworks and tools.
Good knowledge of scripting languages (eg. powershell, vbscript).
Experience in analyzing and maintaining complex legacy source code.
Readiness to work independently.
Automation test experience.
Constructing test-plans and testing scenarios.
Understanding of ML lifecycle (data prep, training, validation, deployment).
Familiarity with Responsible AI principles (bias, fairness, explainability).
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