QA Engineer

QA Engineer

Key Responsibilities:

1. Test Installation and Upgrade Processes in Various Environments: Ensure that the product installs correctly and upgrades seamlessly across different customer environments (private clouds, public clouds, and on-prem data centers). Validate the installation and configuration in Kubernetes (K8s) clusters, including Helm charts, manifests, and custom configurations.
2. Matrix and Environment Testing: Execute tests across various environments, including different cloud providers (AWS, Azure, GCP) ensuring compatibility, performance, and reliability of customer experience. Set up and maintain test environments mirroring customer configurations, including complex networking, storage, and security settings.
3. Support Automation, CI/CD, and Monitoring Pipelines: Ensure that test cases are integrated into the CI/CD pipeline, automating tests for new releases.Create automated testing suites that validate upgrades, scalability, and reliability in production-like environments.
4. Integration Testing with ML-Ops and SaaS Platforms: Test the product's integration with ML-Ops platforms, ensuring smooth interaction between components.Validate integration between the on-premise systems and the SaaS backend, ensuring data consistency, synchronization, and functionality.

Key Skills 

1. Strong Container and Docker Expertise:
1. Experience in deploying, managing, and troubleshooting Docker containers,
2. Working with Helm based Kubernetes deployments  including  managing and updating Helm charts.
2. Automation & CI/CD Pipeline Expertise:
1. Proficiency in setting up, maintaining, and scaling test automation frameworks within CI/CD environments (e.g., Jenkins, CircleCI, GitLab CI).
2. Hands-on experience with test automation tools and frameworks like Playwright, k6 , or equivalent tools for API and integration testing.
3. Cross-Environment Testing and Troubleshooting:
1. Strong troubleshooting and diagnostic skills in complex, multi-component distributed systems, including knowledge of networking, storage, and security configurations.
2. Ability to work with logging aggregation and telemetry (e.g., ELK stack, Prometheus) for distributed systems.
4. Multiple Cloud Familiarity:
1. Proven ability to work across various cloud environments (AWS, Azure, GCP)
5. Experience with ML-Ops and Platform Integrations:
1. Familiarity with ML-Ops platforms (such as Kubeflow, MLflow, etc.) and experience in testing integrations with AI/ML workflows and pipelines..