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.

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