QA Manager

Technical Challenge:

Enterprises using Apache Kafka face escalating cloud costs (up to 40% of infrastructure spend), with teams manually managing broker configurations, idle resources, and client tuning. Superstream solves this by providing autonomous Kafka optimization - detecting misconfigurations, removing idle topics/partitions/consumer groups, and applying zero-code client optimizations to reduce costs by up to 60% while improving stability.

My Role:

• • Manage system testing domain for complex Kafka optimization platform with team of 3 QA engineers
• • Plan and execute comprehensive tests on distributed systems (60% hands-on involvement)
• • Develop test plans for advanced technology stack including AWS MSK, Confluent Cloud, and Aiven
• • Perform functional, integration, performance, and regression testing across multiple environments
• • Identify, resolve, and document defects encountered during testing cycles
• • Collaborate closely with development and engineering teams to improve system quality and integrity
• • Implement test automation processes where applicable using Java, TestNG, and Rest-Assured
• • Train and professionally manage the testing team, providing mentorship and technical guidance
• • Generate detailed reports on testing results with actionable recommendations for improvement
• • Drive process improvements using Agile methodologies and DevOps frameworks
• • Spearheaded adoption of AI-powered quality assurance utilizing GPT/Claude-based models for test case generation, natural language test specifications, and automated root cause analysis of failures

Technical Challenge:

Enterprise-scale Visual Agentic AI platform serving millions of users with real-time computer vision, augmented reality overlay capabilities, and intelligent video analysis. TechSee's AI agents autonomously process live video streams to provide contextual guidance, object recognition, and automated troubleshooting across industries like telecommunications, insurance, and field services. The platform must handle complex scenarios: simultaneous multi-user video sessions, real-time AR annotation rendering, machine learning model inference at scale, and seamless integration with enterprise systems while maintaining sub-second latency for critical visual assistance workflows.

My Role:

• • Lead Software Automation Engineer with full ownership of E2E testing for Visual Agentic AI platform
• • Built complete automation infrastructure from scratch using Java, Selenium, TestNG, JavaScript, Python
• • Managed team of 7 QA engineers - conducted 1-on-1s, performance reviews, skill development programs
• • Direct collaboration with product, R&D, DevOps teams for architecture decisions and test strategy
• • Client-facing responsibilities: conducted demos, gathered requirements, presented testing metrics to stakeholders
• • Designed and implemented CI/CD pipeline with Jenkins - automated test execution on every PR
• • Created comprehensive test coverage: functional, integration, regression, performance
• • Cross-functional leadership: participated in sprint planning, architecture reviews, release planning
• • Established testing best practices, documentation standards, knowledge sharing sessions
• • Mentored team members in test automation, conducted technical interviews for new hires
• • Established AI-driven testing practices using prompt engineering and LLM integration for automated test scenario generation, intelligent test data creation, and anomaly detection in test results

Technical Challenge:

Enterprise-grade Video Platform as a Service (VPaaS) powering OTT media delivery for millions of subscribers with integrated financial services and payment processing. Kaltura's platform handles complex video workflows including live streaming, on-demand content delivery, transcoding pipelines, and DRM protection while simultaneously managing subscription billing, payment gateway integrations, and regulatory compliance (PCI DSS, GDPR). The system must support peak traffic scenarios like Black Friday with 100K+ concurrent users, maintain 99.9% uptime SLA, ensure secure financial transactions, and deliver adaptive streaming across multiple devices and geographical regions with CDN optimization for global content distribution.

My Role:

• • Led team of 8 QA engineers for large-scale VPaaS solution testing
• • Managed complete E2E testing process for complex video platform system
• • Developed comprehensive test plans covering unit, functional, regression, integration, and API testing
• • Executed UAT (User Acceptance Testing) with enterprise clients
• • Implemented performance testing with BlazeMeter and JMeter for high traffic scenarios
• • Set up robust testing system with automated tests integrated in CI/CD pipelines
• • Managed KPIs for QA team using HP QC and Jira for test management
• • Reported on testing progress and results to stakeholders with improvement recommendations
• • Promoted collaboration between QA, development, and engineering teams
• • Applied Agile methodologies in sprint planning and test execution