Job Summary: We are looking for a skilled and detail-oriented QA Engineer with strong experience in both Manual Testing and Test Automation . The ideal candidate will be responsible for end-to-end testing, including test strategy creation, test planning, execution, and automation across Web, Mobile, and API layers. This role requires hands-on expertise in automation tools and frameworks using Java/Python , Selenium , Appium , and experience in API testing. Key Responsibilities: Design and implement comprehensive test strategies and test plans . Perform manual testing for functional, integration, regression, and exploratory test scenarios. Design, develop, and maintain automation test scripts for Web UI, Mobile Apps, and APIs. Role & responsibilities Design and implement comprehensive test strategies and test plans . Perform manual testing for functional, integration, regression, and exploratory test scenarios. Design, develop, and maintain automation test scripts for Web UI, Mobile Apps, and APIs. 4 to 8 years of experience in Software Testing (Manual and Automation). Strong experience in manual testing , test case design, and test strategy planning. Hands-on experience in Web UI automation using Selenium with Java or Python . Experience with Mobile App automation using Appium . Strong experience in API testing using tools like Postman , and automation using REST Assured , Java , or Python . Good understanding of SDLC , STLC , and Agile methodologies . Good to Have: Experience with automation frameworks like Robot Framework , Playwright , or Cypress . Knowledge of CI/CD tools such as Jenkins, GitLab CI, or similar. Familiarity with test reporting tools , bug tracking systems (e.g., JIRA), and version control systems (e.g., Git). Exposure to performance testing tools like JMeter is a plus.
Role & responsibilities AL Model Engineering Expertise: Deep proficiency in AI model engineering, that includes : Training and fine-tuning Vision Language Models (VLM), large language models (LLMs) and other Deep Learning (DL) architectures. Model optimization, quantization, and deployment for latency and throughput. Model evaluation and monitoring in production environments. Creating Small Language Models using Distillation , Pruning etc. Worked on accelerating AI training and inference, with GPU and NPU on Compute, Edge and Mobile platforms with SOCs from NVIDIA, Qualcomm etc. AI Solution Development: Build and integrate AI capabilities (e.g. computer vision, NLP, inferencing , recommendation, summarisation) into production-grade Web/Mobile App software. Scalability & Performance: Experience scaling AI systems for high-volume, real-time applications, leveraging Cloud-Native or Edge-AI technologies. Data-Driven Development: Strong understanding of data pipelines and feature engineering for AI applications.