Description and Requirements
- 5+ years of experience in software development with a focus on AI, machine learning, or related fields.
- Strong experience in design, develop, and implement AI-based testing solutions and accelerators to automate and optimize various aspects of the software testing process.
- Proven experience with integrating AI/ML algorithms into production-grade systems, preferably in the context of test automation or quality assurance.
- Build machine learning models that can automatically generate test cases, identify anomalies, and predict defects.
- Integrate AI/ML technologies into test automation frameworks to improve the efficiency, accuracy, and coverage of tests (e.g., automated test generation, intelligent test execution, test prioritization).
- Develop AI-powered test analytics dashboards to provide insights into test coverage, performance, and potential bottlenecks.
- Apply machine learning and deep learning techniques to create predictive models that can anticipate defects, performance issues, or vulnerabilities in software systems.
- Use natural language processing (NLP) to enable AI systems to understand and generate test cases from requirements, user stories, or specifications.
- Continuously improve AI models using feedback from test results, client data, and industry trends.
- Build data pipelines for training and optimizing machine learning models.
- Work closely with QA engineers, product managers, and software developers to understand testing needs and collaborate on developing AI-driven solutions tailored to client requirements.
- Collaborate with DevOps and CI/CD teams to integrate AI-powered testing solutions into existing continuous integration and continuous testing workflows.
- Provide guidance and technical expertise to the testing team on how AI can enhance testing practices and processes.
- Customize AI-based testing tools and accelerators to fit client-specific needs and test environments.
- Develop automation scripts using AI-driven approaches for test data generation, test execution, and reporting.
- Optimize test execution times by using AI to intelligently select, schedule, and prioritize tests based on factors like code changes, risk, and business impact.
- Stay current with developments in AI, machine learning, and the testing industry to incorporate new methodologies and techniques into AI-based testing solutions.
- Experiment with new AI algorithms and technologies to improve the quality and performance of software testing tools.
- Contribute to internal research and development efforts aimed at creating next-generation AI-powered testing frameworks.
- Provide pre-sales technical support by demonstrating AI-based testing solutions and how they can solve specific customer pain points.
- Assist customers with implementing and customizing AI-driven test solutions, ensuring successful deployment and integration into their environments.
- Troubleshoot and resolve issues related to AI-based testing systems, including debugging AI models, improving test results, and optimizing performance.
- Create technical documentation, including AI model architecture, integration guides, and user manuals for internal and client-facing teams.
- Share knowledge through internal workshops, presentations, and code reviews to help educate the team on AI-based testing approaches.
Additional Job Description Technical Skills:
- Expertise in machine learning frameworks such as TensorFlow, PyTorch, Keras, or Scikit-learn.
- Hands-on experience with AI technologies such as natural language processing (NLP), anomaly detection, deep learning, and neural networks.
- Proficiency in programming languages like Python, Java, or C++ (Python is preferred).
- Experience with test automation frameworks such as Selenium, Appium, or similar tools.
- Familiarity with test management tools such as Jira, TestRail, or similar.
- Experience with CI/CD tools like Jenkins, GitLab CI, or Azure DevOps to integrate AI-based testing solutions.
AI and Testing Knowledge:
- Understanding of AI and machine learning principles, including supervised and unsupervised learning, model training, feature engineering, and evaluation metrics.
- Familiarity with traditional software testing methodologies (functional, regression, performance, security) and how AI can enhance these processes.
- Knowledge of intelligent test execution, test optimization, and test automation techniques powered by AI.
