ProductSquads
1 month ago
Sr QA Manager
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Company information
- Company
- ProductSquads
- Location
- India, Gujarat, Ahmedabad India
- Posted
- 1 month ago
Job description
Job Summary
We are looking for an experienced Senior QA Manager to lead our Quality Assurance function for modern web applications and AI-powered products. The ideal candidate will have extensive experience in manual and automation testing, team leadership, test strategy, and AI/LLM application validation. This role requires driving quality across the entire software development lifecycle while mentoring QA teams, collaborating with cross-functional stakeholders, and implementing AI-driven testing practices to ensure high-quality software delivery.
Key Responsibilities
QA Leadership & Strategy
- Define and execute the overall QA strategy aligned with business and engineering objectives.
- Lead, mentor, and grow a team of QA Engineers, Senior QA Engineers, and SDETs.
- Establish quality standards, testing best practices, and automation frameworks across projects.
- Drive continuous improvement initiatives to enhance product quality and testing efficiency.
- Manage resource planning, sprint planning, workload distribution, and performance reviews.
AI & LLM Application Testing
- Define comprehensive testing strategies for AI-powered applications and Large Language Model (LLM) integrations.
- Oversee validation of AI-generated responses for accuracy, consistency, relevance, and safety.
- Ensure conversational AI applications maintain context, logical flow, and expected behavior.
- Review prompt engineering test cases and edge-case scenarios for AI-powered features.
- Validate AI application performance, usability, and response quality across different business use cases.
- Hands on with agentic solutions in AI section
Test Planning & Execution
- Own end-to-end test planning, execution, monitoring, and reporting across multiple products.
- Review and approve detailed test plans, test cases, automation scripts, and test reports.
- Ensure complete functional, regression, integration, system, API, performance, and user acceptance testing.
- Drive risk-based testing approaches for critical business applications.
Automation & Quality Engineering
- Define and drive automation strategy using modern automation frameworks.
- Improve automation coverage and CI/CD integration.
- Ensure automation suites are scalable, maintainable, and continuously executed.
- Promote shift-left testing practices and quality engineering culture.
API & Integration Testing
- Oversee API testing using tools such as Postman, REST Assured, and similar frameworks.
- Validate integrations between AI services, backend systems, databases, third-party platforms, and frontend applications.
- Ensure data consistency and end-to-end system validation.
Defect Management & Quality Metrics
- Establish defect management processes and quality gates.
- Track QA KPIs including:
- Test Coverage
- Defect Leakage
- Automation Coverage
- Regression Effectiveness
- Production Defects
- Test Execution Progress
- Present quality metrics and risk assessments to senior leadership.
Process Improvement
- Standardize QA processes across teams.
- Introduce AI-powered testing tools to improve productivity.
- Drive adoption of modern testing methodologies and best practices.
- Ensure compliance with SDLC, STLC, Agile, and DevOps practices.
Required Qualifications
Education
Bachelor's or Master's degree in Computer Science, Engineering, Information Technology, or a related technical field.
Experience
- 7–15+ years of experience in Software Quality Assurance.
- Minimum 3–5 years of experience leading QA teams.
- Strong experience managing enterprise-scale testing initiatives.
- Experience testing AI-enabled or LLM-based applications is highly preferred.
Required skills
- accuracy
- engineering
- safety
- quality standards
- monitoring
- databases
- reporting
- quality assurance
- team leadership
- information technology
- sdlc
- continuous improvement
- prompt engineering
- agile
- process improvement
- performance reviews
- automation testing
- sprint planning
- bachelor's degree
- best practices
- postman
- computer science
- master's degree
- web applications
- risk assessments
- resource planning
- software development lifecycle
- product quality
- devops practices
- backend systems
- user acceptance testing
- senior leadership
- api testing
- rest assured
- manual testing
- test reports
- performance testing
- automation frameworks
- test strategy
- test cases
- system testing
- automation scripts
- usability
- test planning
- test execution
- integration testing
- regression testing
- software quality assurance
- functional testing
- consistency
- test coverage
- defect management
- relevance
- quality metrics
- ai-powered features
- productivity improvement
- ci/cd integration
- qa teams
- testing best practices
- cross-functional stakeholders
- quality gates
- qa manager
- qa engineers
- stlc
- risk-based testing
- ai-driven testing
- workload distribution
- context
- ai-generated responses
- data consistency
- llm integrations
- production defects
- frontend applications
- llm-based applications
- qa strategy
- shift-left testing
- third-party platforms
- ai-powered products
- automation strategy
- multiple products
- edge-case scenarios
- response quality
- defect leakage
- business use cases
- ai/llm application validation
- high-quality software delivery
- qa leadership
- engineering objectives
- senior qa engineers
- sdets
- testing efficiency
- ai & llm application testing
- large language model (llm)
- conversational ai applications
- logical flow
- expected behavior
- ai application performance
- agentic solutions
- detailed test plans
- critical business applications
- automation & quality engineering
- modern automation frameworks
- automation coverage
- scalable automation suites
- maintainable automation suites
- continuously executed automation
- quality engineering culture
- api & integration testing
- end-to-end system validation
- defect management processes
- qa kpis
- regression effectiveness
- test execution progress
- quality metrics presentation
- standardized qa processes
- ai-powered testing tools
- modern testing methodologies
- leading qa teams
- enterprise-scale testing initiatives
- ai-enabled applications
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