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.
