AI Tester / AI Quality Assurance Engineer

Number of Positions: 2 Education Qualification: B.E/B.Tech. /M.C.A./Graduate (IITs/NITs and BITS graduates preferred) Roles & Responsibilities Design and execute test strategies for AI/ML models, data pipelines, and AI-enabled applicationsValidate data quality, data drift, feature integrity, and training/validation datasetsTest model accuracy, precision, recall, bias, fairness, and explainabilityPerform functional, integration, regression, and non-functional testing for AI systemsAutomate AI testing using Python, test frameworks, and MLOps pipelinesMonitor model performance post-deployment and identify degradation or driftValidate AI outputs against business rules and real-world scenariosCollaborate with data scientists, ML engineers, and product teamsEnsure compliance with AI governance, privacy, and regulatory standardsDocument test cases, test results, risks, and quality metrics Required Skills & Qualifications Strong understanding of Software Testing and QA processesKnowledge of Machine Learning concepts (supervised/unsupervised learning, NLP, CV basics)Experience with Python and data analysis libraries (Pandas, NumPy, Scikit-learn)Familiarity with AI testing techniques: bias testing, adversarial testing, model validationExperience with automation tools and CI/CD pipelinesUnderstanding of data validation, data drift, and model monitoringKnowledge of API testing and cloud platforms (AWS, Azure, GCP) is a plusStrong analytical, problem-solving, and communication skills Preferred Experience Experience with MLOps toolsExposure to Responsible AI, explainability tools, and ethical AI practicesExperience testing GenAI systems (LLMs, chatbots, prompt validation, hallucination checks) Apply Now Number of Positions: 2 Education Qualification: B.E/B.Tech. /M.C.A./Graduate (IITs/NITs and BITS graduates preferred) Roles & Responsibilities Design and execute test strategies for AI/ML models, data pipelines, and AI-enabled applicationsValidate data quality, data drift, feature integrity, and training/validation datasetsTest model accuracy, precision, recall, bias, fairness, and explainabilityPerform functional, integration, regression, and non-functional testing for AI systemsAutomate AI testing using Python, test frameworks, and MLOps pipelinesMonitor model performance post-deployment and identify degradation or driftValidate AI outputs against business rules and real-world scenariosCollaborate with data scientists, ML engineers, and product teamsEnsure compliance with AI governance, privacy, and regulatory standardsDocument test cases, test results, risks, and quality metrics Required Skills & Qualifications Strong understanding of Software Testing and QA processesKnowledge of Machine Learning concepts (supervised/unsupervised learning, NLP, CV basics)Experience with Python and data analysis libraries (Pandas, NumPy, Scikit-learn)Familiarity with AI testing techniques: bias testing, adversarial testing, model validationExperience with automation tools and CI/CD pipelinesUnderstanding of data validation, data drift, and model monitoringKnowledge of API testing and cloud platforms (AWS, Azure, GCP) is a plusStrong analytical, problem-solving, and communication skills Preferred Experience Experience with MLOps toolsExposure to Responsible AI, explainability tools, and ethical AI practicesExperience testing GenAI systems (LLMs, chatbots, prompt validation, hallucination checks) Apply Now