Senior Quality Assurance Engineer

Job Title : Senior QA Engineer

Location : Onsite - Bangalore (Hybrid) - General Shift

Budget - 30 LPA Max

• Proficient in Automation Testing with Generative AI

Role Overview :

We are looking for a Senior / Lead QA Engineer with hands-on experience in end-to-end testing of

AI / ML-powered and cloud-based media platforms .

The ideal candidate is not only technically proficient but also AI-aware in their QA approach

informed on the latest AI test strategies, conversant with AI-powered tools for test case generation and execution , and comfortable experimenting with new AI-enabled frameworks that enhance automation efficiency.

You will help

set up intelligent QA systems integrated into CI / CD pipelines , driving test automation and turnaround time (TAT) efficiencies across the team.

Key Responsibilities :

Own QA strategy, planning, and

execution for AI / ML applications and microservices within the platform .

Design, automate, and optimize test cases across functional, integration, regression, and performance testing.

Validate AI / ML model outputs for accuracy, consistency, and bias; collaborate with data scientists to fine-tune test strategies for non-deterministic systems.

Stay informed on AI-driven testing approaches and tools; apply them to enhance test coverage and reduce manual effort.

Experiment with and

onboard AI-enabled testing frameworks that support CI / CD linked automation and continuous validation .

Build and maintain intelligent test automation systems that improve overall system efficiency and TAT.

Test end-to-end content workflows involving video, audio, and text transcoding, speech-to-text, computer vision, content QC, and metadata enrichment.

Develop and maintain automated test frameworks using tools like Playwright, PyTest, Selenium, Postman, JMeter, or Robot Framework.

Integrate QA seamlessly into CI / CD pipelines (Jenkins, GitHub Actions, or similar).

Collaborate closely with engineering, DevOps, and product teams to reproduce and resolve defects efficiently.

Contribute to improving QA standards, documentation, and test data management practices.

Support performance benchmarking, scalability, and regression testing of AI-enabled workflows.

Required Skills & Experience :

6 to 10 years of QA experience in product development or enterprise platform environments.

Strong understanding of SDLC, STLC, Agile methodologies, and defect lifecycle.

Proven experience in API testing, automation, and performance testing of distributed or cloud-based systems.

Familiarity with AI test strategies and exposure to AI / ML testing tools (for model validation, dataset evaluation, or automated test generation).

Comfort with exploring and integrating new AI-driven frameworks to accelerate QA automation.

Familiarity with media domain concepts (audio / video codecs, subtitles, speech-to text, NLP, or computer vision) preferred.

Working knowledge of databases (SQL / NoSQL), scripting (Python preferred), and test automation frameworks.

Experience with cloud platforms (AWS / Azure / GCP) and CI / CD pipelines.

Strong analytical, documentation, and debugging skills.

Excellent communication and collaboration skills across cross-functional teams.