We are seeking a skilled and detail-oriented AI Test Engineer to join our team working on cutting-edge AI agents. As a tester, you will play a critical role in ensuring the quality, reliability, and performance of our AI systems. You will design and execute test cases, identify and report bugs, and collaborate with developers and data scientists to improve the overall functionality of our AI agents.
The ideal candidate has a strong background in software testing, a solid understanding of AI/ML concepts, and experience working with AI-driven systems. You should be passionate about quality assurance, have a keen eye for detail, and be able to think critically to identify edge cases and potential failures in AI behavior.
Key Responsibilities:
Test Planning and Strategy:
Develop comprehensive test plans and strategies for AI agents, including functional, regression, performance, and edge-case testing.
Collaborate with developers and data scientists to understand AI agent behavior and define acceptance criteria.
Test Case Design and Execution:
Design and execute test cases to validate the functionality, accuracy, and performance of AI agents.
Test AI agent responses, decision-making processes, and interactions with users or other systems.
Data Validation:
Verify the quality and accuracy of training data used for AI models.
Test the AI agent’s ability to handle diverse and unexpected inputs.
Performance and Scalability Testing:
Evaluate the performance of AI agents under various conditions, including high load and edge cases.
Identify bottlenecks and work with the team to optimize system performance.
Bug Reporting and Tracking:
Identify, document, and track bugs using issue-tracking tools (e.g., Jira, Trello).
Work closely with developers to ensure timely resolution of issues.
Automation:
Develop and maintain automated test scripts for AI agent testing.
Integrate automated tests into CI/CD pipelines for continuous testing.
Collaboration:
Work closely with cross-functional teams, including developers, data scientists, and product managers, to ensure alignment on quality standards.
Participate in sprint planning, stand-ups, and retrospectives to provide QA insights.
Required Skill Sets:
Technical Skills:
Strong understanding of software testing methodologies (e.g., functional, regression, performance, and usability testing).
Familiarity with AI/ML concepts, including natural language processing (NLP), reinforcement learning, and neural networks.
Experience with test automation tools (e.g., Selenium, pytest, or similar).
Proficiency in programming languages such as Python, Java, or JavaScript for writing test scripts.
Knowledge of API testing tools (e.g., Postman, REST Assured) to test AI agent integrations.
Experience with CI/CD pipelines and tools like Jenkins, GitLab CI, or GitHub Actions.
AI-Specific Testing Skills:
Ability to test and validate AI agent behavior, including decision-making, response accuracy, and adaptability.
Experience with data validation and testing AI models for bias, fairness, and robustness.
Familiarity with AI testing frameworks (e.g., TensorFlow Extended (TFX), MLflow, or proprietary tools).
Soft Skills:
Strong analytical and problem-solving skills to identify edge cases and potential failures.
Excellent communication skills to collaborate with cross-functional teams and document test results.
Attention to detail and a commitment to delivering high-quality results.
Nice-to-Have Skills:
Experience with cloud platforms (e.g., AWS, Azure, GCP) for testing AI agents deployed in cloud environments.
Knowledge of containerization tools (e.g., Docker, Kubernetes) for testing AI agents in containerized environments.
Familiarity with version control systems (e.g., Git) and collaborative tools (e.g., Jira, Confluence).
Qualifications:
Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
5+ years of experience in software testing, with at least 1-2 years of experience in testing AI/ML systems.
Relevant certifications in software testing (e.g., ISTQB) or AI/ML (e.g., TensorFlow Developer Certificate) are a plus.
Software testing, AI/ML concepts, test automation tools, programming languages such as Python, AI testing frameworks (e.g., TensorFlow Extended (TFX), MLflow, or proprietary tool