1)Supervise the examination candidates with due diligence.2)Maintaining the academic integrity of the examinations.3)Interact with the Examination Coordinators and other designated staff of the AL as necessary to manage questions or concerns of the employee or students during the examinations.

• Refactored and developed efficient backend REST APIs in Python to retrieve customer and product data on a live Ecommerce website operating on a scale of 200,000 concurrent users. • Achieved an average API response time less than 120ms which was a 96% improvement on the existing performance.• Resolved up to 10 UI/UX and backend functionality related issues and bugs on a daily basis which was tracked and documented on JIRA.• Created interactive dashboards with Microsoft PowerBI, fusing intricate datasets to facilitate data-driven solutions across a range of client requirements which resulted in a 37% reduction in overall project cost.

• Patched a significant number of bugs and vulnerabilities to fortify the existing infrastructure and wrote around 200 unit test cases to achieve ‘A’ grade in code maintainability and a high of 89% in the code coverage metrics on SonarQube.• Wrote and executed complex SQL queries in Redshift to create, retrieve and process over 200 generated user data tables.• Developed a 10-stage parameterized Jenkins CI/CD ETL pipeline combined with Apache Airflow to integrate a custom data pipeline from data creation to data storage on a data warehouse to migrate up to 500 TB of data daily.• Received the “Shared Success Catalyst” award for contributions to the project in development, optimization and testing.

Here the classification goal is to predict whether the patient has 10-year risk of future coronary heart disease (CHD). Classification model was used to predict the heart disease in a subject.

• Researched Natural Language Processing algorithms for text summarization by referring to various technical publications and white papers of the same. • Created a Python project using different libraries and packages such as Pandas, NumPy, and SciPy to implement the TF-IDF algorithm for text summarization which involved recognition of over 10000 words at an accuracy of 92%.