Senior Engineer-Test Automation

TEKsystems•Atlanta, GA

4d•Remote

About The Position

Think of TEKsystems Global Services (TGS) as the growth solution for enterprises today. We unleash growth through technology, strategy, design, execution and operations with a customer-first mindset for bold business leaders. We deliver cloud, data and customer experience solutions. Our partnerships with leading cloud, design and business intelligence platforms fuel our expertise. We value deep relationships, dedication to serving others and inclusion. We drive positive outcomes for our people and our business, and we stay true to our commitments and act in harmony with our words. We exist to create significant opportunity for people to achieve fulfillment through career success. Ready to join us? Here’s what the opportunity supported through our TGS Talent Acquisition Team requires: Job Summary We are seeking a highly skilled and motivated Senior Automation Test Engineer with 8–10 years of experience in software testing and test automation, including at least 2 years focused on AI Testing/Evaluation. The ideal candidate will have hands-on expertise in designing, developing, and executing robust automated test frameworks and pipelines, ensuring the quality and reliability of AI/ML models and software systems. The ideal candidate should have strong proficiency in Selenium and Playwright for UI/API and end-to-end test automation, as well as excellent programming skills in Python and Java. A solid foundation in software quality assurance methodologies, data validation (including working knowledge of SQL), and integrating automated tests within CI/CD pipelines and DevOps processes is essential. The ideal candidate should be a team player, responsible for the design and orchestration of automated testing for AI components of various solutions delivered by our Application, Data & A/I Practice for clients, ensuring high standards of quality and performance in production environments.

Requirements

Bachelor’s degree in Computer Science, Engineering, or a related field
8–10 years of hands-on experience in software Quality Assurance (QA) and test automation, with a proven track record in designing and implementing automated testing solutions for complex applications.
Minimum 2 years of recent experience in AI/ML testing or evaluation, validating machine learning models or AI-driven features (e.g., testing model outputs, performance, bias, and reliability).
Strong proficiency in Python and Java, with the ability to write clean, efficient automated test scripts and frameworks in both languages.
Extensive experience with test automation tools and frameworks, especially Selenium and Playwright, for web UI automation and API testing (experience with similar tools like Cypress or TestComplete is a plus).
Solid understanding of SQL and relational databases for test data setup and result validation; able to write and execute complex SQL queries to verify data transformations and backend processes.
Hands-on experience with Continuous Integration/Continuous Delivery (CI/CD) processes and tools (e.g., Jenkins, GitHub Actions, Azure DevOps) – including integrating automated tests in pipelines, managing test environments, and using version control (Git) for test code.
Familiarity with DevOps practices and tools (Docker, Kubernetes, etc.) as they relate to deploying and running tests in various environments; ability to collaborate with DevOps teams to ensure smooth automation execution in build pipelines.
In-depth knowledge of software testing methodologies (unit, integration, system, UAT, performance, etc.) and QA best practices, including test case design techniques and quality metrics.
Strong analytical and problem-solving skills to troubleshoot complex issues across application, data, and infrastructure layers – especially in diagnosing test failures related to AI models or data anomalies.
Excellent verbal and written communication skills, with the ability to document test plans, report defects/results clearly, and translate technical issues and quality metrics into actionable insights for both technical teams and non-technical stakeholders.
Ability to work effectively cross-functionally with product managers, developers, data scientists, and other engineering teams, ensuring a shared understanding of quality goals and delivering a high-quality product.
Passion for continuous learning and staying updated on the latest trends in test automation, AI/ML technologies, and quality engineering. Demonstrated ability to quickly learn and adopt new tools or methodologies to enhance testing effectiveness.

Nice To Haves

Industry-recognized certifications in Quality Assurance or Testing (e.g., ISTQB Advanced Level or equivalent).
Experience with AI/ML frameworks (such as TensorFlow, PyTorch, or scikit-learn) or data science tools, which can help in designing better test scenarios for machine learning models and understanding model pipeline intricacies.
Exposure to AI-specific testing tools or techniques – for instance, using frameworks for model validation, bias detection, or AI-driven test generation – and familiarity with concepts like model drift, adversarial testing, and fairness in AI.
Experience in performance and load testing of web applications or AI services (using tools like JMeter, LoadRunner, or Locust) to ensure systems can handle required scale and performance benchmarks.
Familiarity with cloud platforms (AWS, GCP, Azure) and testing applications or services deployed in the cloud, including understanding how to set up test environments in cloud and utilize cloud-based testing services.

Responsibilities

Actively participate in requirement gathering and design discussions, translating functional requirements into comprehensive test plans and strategies, and clearly conveying complex quality considerations to stakeholders.
Design, develop, and maintain end-to-end test automation frameworks using Selenium and Playwright, ensuring extensive coverage of web application interfaces, API integration and AI-driven features across different browsers and environments.
Utilize strong programming skills in Python and Java to create and optimize test scripts; employ SQL to set up and validate test data and to verify backend data integrity and transformations in databases as part of the testing process.
Implement and integrate automated testing within CI/CD pipelines (e.g., using Jenkins, Azure DevOps, or similar) to enable continuous testing and rapid delivery. Ensure that every code change triggers automated tests and that quality gates are enforced before deployment.
Collaborate closely with software developers, data engineers, data scientists, and DevOps engineers throughout the development lifecycle to ensure test cases cover all critical scenarios and that defects are promptly identified, documented, and resolved.
Develop and execute a variety of automated tests (unit, integration, regression, performance, and security tests) to validate AI/ML models and data pipelines – including verifying model predictions, evaluating accuracy against expected outcomes, and testing model behavior under various scenarios.
Monitor and analyze test execution results for both traditional software and AI-specific metrics (such as model precision, recall, and performance), proactively identifying issues like regression, data drift, or anomalies in model outputs, and drive continuous improvements in testing processes.
Apply best practices in test data management and environment configuration to simulate real-world data scenarios for AI systems, including the use of synthetic data generation or mocking techniques to robustly test machine learning models and data workflows.
Ensure that testing practices adhere to relevant quality standards and compliance requirements (especially when handling sensitive data in AI model testing) and implement necessary DevOps and security testing (DevSecOps) measures such as automated checks for data privacy or model bias, where applicable.
Occasional support to Sales and Pre-Sales teams by providing thought leadership in quality assurance for AI solutions – for example, helping demonstrate our testing approach and tools to clients or crafting points on how robust test automation drives reliable AI solution delivery.