QA Manager, Software Engineering
Penguin Solutions (company site, opens in a new tab)12+ yrsBengaluruOn-siteDirect career pageEst. 30–50 LPA
Posted 10h ago
Reach people at Penguin Solutions
People at this company you can reach
Log in to see who works here
Create a free account or log in to see the people at Penguin Solutions you could write to:
- Recruiters & HR
- People in this function
- Work addresses
Your match
Against your primary resume
Log in to see your match score
Create a free account or log in to unlock your CV match score across:
- Role fit
- Experience
- Industry relevance
Protocol Intelligence
How competitive this role is
Competition
Background fit
Strictness
Log in to see why each signal reads the way it does.
Job description
Structured overview of role & requirements
About This Role
- Own end-to-end Quality Engineering strategy for ClusterWareAI, covering functional, regression, performance, scalability, security, and reliability testing across distributed AI infrastructure.
- Lead and develop a Quality Engineering team, driving automation-first quality culture and scalable test automation frameworks integrated with CI/CD pipelines.
- Establish AI Quality Engineering strategy including automated AI evaluation, regression pipelines, adversarial testing, and production AI quality metrics for LLM and agentic AI capabilities.
Minimum Requirements
- Bachelor's degree in Computer Science, Engineering, or equivalent practical experience.
- Minimum 12 years in Quality Engineering, Software Engineering, or related roles, with at least 3 years leading QE teams.
- Strong programming and automation skills, preferably Python, with experience building scalable automation frameworks for distributed systems, cloud, Kubernetes, and infrastructure software.
- Work Experience Required: 12+ years in relevant roles with 3+ years in leadership. Location: Hybrid in Bangalore, India.
Ideal Candidate Profile
- Experienced in quality engineering for complex, distributed cloud/infrastructure platforms with deep understanding of testing infrastructure lifecycle, GPU infrastructure, Kubernetes, and workload orchestration.
- Demonstrated expertise in AI/LLM quality engineering including evaluation methodologies, regression pipelines, adversarial and safety testing for AI agents and LLM applications.
- Proven technical leadership with hands-on involvement in automation, CI/CD integration, performance/scalability testing, and cross-team collaboration in product engineering environments.
