Verified Job
IT / Software / Data Analyst
Quality Assurance Engineer- job post Expertshub.ai
IT / Software / Data Analyst
#795010
Remote / WFH
Expertshub.ai
Job Description
Full job description
AI Quality Assurance (QA) Engineer
ROLE OVERVIEW
Educational Qualifications
B.Tech. or M.Sc. in Computer Science, Data Science, or a related discipline.
Certification in quality assurance, software testing, or test automation is preferred.
Experience
4–6 years of quality assurance experience for AI/ML systems, analytics platforms, or data-driven applications.
Hands-on experience in functional, performance, data-validation, and model-output testing.
Familiarity with testing approaches for NLP, computer vision, and data-centric applications.
Key Responsibilities
Design and execute end-to-end testing strategies for AI services, covering functionality, performance, data quality, model accuracy, fairness, security, and compliance.
Create test plans, test cases, test data, regression suites, and acceptance criteria tailored to AI/ML applications and government use cases.
Validate model outputs against business requirements, reference datasets, accuracy thresholds, and expected operating conditions.
Conduct fairness testing, bias detection, subgroup analysis, and Responsible AI compliance checks.
Maintain defect logs, evidence, issue severity, root-cause details, and resolution tracking across development, staging, and production environments.
Collaborate with data scientists, ML engineers, business analysts, and product teams to establish model-testing protocols and release gates.
Build and execute automated test suites within CI/CD pipelines for AI services and data workflows.
Validate APIs, microservices, integrations, data pipelines, ETL processes, and database outputs.
Monitor production AI services for performance degradation, model drift, accuracy drift, data-quality failures, and compliance exceptions.
Support privacy, access-control, encryption, audit, and security validation activities.
Technical Competencies
AI Testing: model validation, output verification, bias detection, fairness testing, Responsible AI testing, and drift monitoring.
Test Automation: Selenium, pytest, TestNG, Cypress, and CI-based automated test execution.
Programming and Data: Python for test scripting, SQL for data validation, and basic statistical testing concepts.
Testing Tools: Jira, TestRail, Postman, Jenkins, or equivalent tools.
Data Validation: ETL testing, database testing, data-quality checks, reconciliation, and pipeline validation.
Performance Testing: load, stress, scalability, latency, and throughput testing for AI and data-processing services.
Security Testing: privacy validation, role-based access testing, encryption checks, and audit support.
Cloud and API Testing: AWS, Azure, or GCP environments; REST, GraphQL, microservices, and integration testing.
Work Location: In person
AI Quality Assurance (QA) Engineer
ROLE OVERVIEW
Educational Qualifications
B.Tech. or M.Sc. in Computer Science, Data Science, or a related discipline.
Certification in quality assurance, software testing, or test automation is preferred.
Experience
4–6 years of quality assurance experience for AI/ML systems, analytics platforms, or data-driven applications.
Hands-on experience in functional, performance, data-validation, and model-output testing.
Familiarity with testing approaches for NLP, computer vision, and data-centric applications.
Key Responsibilities
Design and execute end-to-end testing strategies for AI services, covering functionality, performance, data quality, model accuracy, fairness, security, and compliance.
Create test plans, test cases, test data, regression suites, and acceptance criteria tailored to AI/ML applications and government use cases.
Validate model outputs against business requirements, reference datasets, accuracy thresholds, and expected operating conditions.
Conduct fairness testing, bias detection, subgroup analysis, and Responsible AI compliance checks.
Maintain defect logs, evidence, issue severity, root-cause details, and resolution tracking across development, staging, and production environments.
Collaborate with data scientists, ML engineers, business analysts, and product teams to establish model-testing protocols and release gates.
Build and execute automated test suites within CI/CD pipelines for AI services and data workflows.
Validate APIs, microservices, integrations, data pipelines, ETL processes, and database outputs.
Monitor production AI services for performance degradation, model drift, accuracy drift, data-quality failures, and compliance exceptions.
Support privacy, access-control, encryption, audit, and security validation activities.
Technical Competencies
AI Testing: model validation, output verification, bias detection, fairness testing, Responsible AI testing, and drift monitoring.
Test Automation: Selenium, pytest, TestNG, Cypress, and CI-based automated test execution.
Programming and Data: Python for test scripting, SQL for data validation, and basic statistical testing concepts.
Testing Tools: Jira, TestRail, Postman, Jenkins, or equivalent tools.
Data Validation: ETL testing, database testing, data-quality checks, reconciliation, and pipeline validation.
Performance Testing: load, stress, scalability, latency, and throughput testing for AI and data-processing services.
Security Testing: privacy validation, role-based access testing, encryption checks, and audit support.
Cloud and API Testing: AWS, Azure, or GCP environments; REST, GraphQL, microservices, and integration testing.
Work Location: In person
