​​Testing Expertise:

• Possess a strong technical background and a deep understanding of the project's technology stack.

• Collaborate with technical leads and architects to make informed decisions.

• Address technical challenges and facilitate solutions.

• Diligently handle new feature development, conducting impact analysis, coordinating with Product Manager for the development of the new features.

• Troubleshooting and resolution of production issues.

• Take ownership of the deployment and improve deployment process.

• Work on technical debt by upgrading various framework/libraries that would improve performance and security.

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Automation-First Approach:

• Develop, maintain, and extend a fully automated testing suite that covers unit, integration, performance, and end-to-end testing.

• Emphasize automation to minimize manual intervention and maximize test coverage, reliability, and repeatability.

DevOps & CI/CD Integration:

• Collaborate closely with DevOps to ensure all tests (including those for model deployment and data pipelines) are tightly integrated with modern CI/CD workflows. 

• Streamline rapid yet safe releases through automation and timely feedback.

Automated Testing Frameworks:

• Extensive hands-on experience with frameworks such as Pytest (Python testing), Playwright (end-to-end browser testing), Postman (API testing), and Langfuse (LLM output tracking/testing).

• Implement and maintain robust API contract testing to ensure reliable interactions between services.

Manual & LLM Testing:

• Execute manual test cases with strong attention to detail, especially for evaluating Large Language Model (LLM) output quality. 

• Flag issues such as hallucinations, factual inaccuracies, or unexpected edge case responses. 

• Continuously update manual testing strategies to adapt to evolving model behaviors and business requirements. 

Monitoring, Observability & Post-Deploy Quality: 

• Configure, deploy, and interpret dashboards from monitoring tools like Prometheus, Grafana, and CloudWatch. 

• Track model health, pipeline performance, error rates, and system anomalies after deployment. 

• Proactively investigate and triage quality issues uncovered in production. 
   

Core Abilities and Technical Skills:

• Deep practical knowledge in test automation, performance, and reliability engineering. 

• In-depth experience integrating tests into CI/CD pipelines, especially for machine learning and AI model workflows. 

• Hands-on proficiency in automated QA tools: Pytest, Playwright, Postman, Langfuse, and similar. 

• Solid foundation in manual exploratory testing, particularly for complex and evolving outputs such as those from LLMs. 

• Expertise in monitoring, APM, and observability tools (e.g., Prometheus, Grafana, CloudWatch). 

• Demonstrated strong problem-solving skills—anticipate, identify, and resolve issues early. 

• Strong communication skills to clearly articulate requirements, quality risks, and advocate for automation-driven quality throughout the organization.

Mindset:

• Automation-First: Relentless emphasis on driving automation over manual effort. 

• Proactive: Anticipates issues and testing needs; does not wait to be told what to test. 

• Quality Advocate: Champions testing best practices and designs processes to catch bugs before production. 

• Curious & Continuous Learner: Seeks out new tools, stays current with testing frameworks and industry best practices. 

• Collaborative: Partners effectively with product, engineering, and DevOps teams to deliver high-quality models and features at scale.​

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