Lead Software Quality Developer (SDET)

Job Description: • Research, design, and develop computer software or specialized utility programs. • Analyze user needs and develop software solutions, applying principles and techniques of computer science. • Liaise with business analysts and development team for clarification and understanding of requirements. • Design and develop comprehensive quality assurance strategies and frameworks. Implement automated testing systems to ensure reusability and efficiency for functional and non-functional testing using open source libraries. • Design and implement comprehensive test plans and test cases based on requirements and design documentation. Develop automated test systems to ensure reusability and efficiency. • Perform various types of testing, including functional, integration, regression, and performance testing. Analyze and track down error root causes, providing detailed reports and recommendations for improvement. • Promote and implement testing methodologies, aiming to identify and address defects early in the development lifecycle. • Perform root cause analysis. • Update & Track defects in JIRA. Requirements: • Master or Bachelor’s degree in Computer Science/ Information Technology/ Programming & Systems Analysis/ Science (Computer Studies) faculties. • 12+ years of experience as a Java or QA Developer. • Cloud Certification(s). Technical: • Proficiency in programming languages such as Java, Python, and JavaScript. • Experience with automation testing tools including Selenium, Rest Assured, Cypress and Cucumber, as well as Bigdata/ETL Testing with Hadoop and Pyspark. • Familiarity with build tools like Maven/sbt/ant, UML, Restful web services, Jenkins/Team City, Source management – SVN/GIT, TDD using Junit. • Expertise in DevOps tools & practices such as CI/CD pipelines, Gitlab, Jenkins, and Containerization technologies: Docker and Kubernetes. • Working knowledge of Cloud Architecture & Operating models, preferably AWS. • Awareness of Artificial Intelligence, Machine Learning, and Data Analytics with ability to leverage them in real-time scenarios. • Understanding of AI/ML fundamentals including prompt engineering, model limitations, and best practices for human-AI collaboration. • Hands on knowledge of desktop based app and its automation. Functional: • Knowledge on Capital Markets Domain - like Front to back trading lifecycles , Regulatory Compliance framework. • Experience of systems dealing with Counterparty Risk, Market Risk, credit limits, monte carlo risk simulation methodology and its implementation eg: VaR, Stress, P&L reporting.