Developed an automated, self-service, multi-tenant content-risk management system utilizing Java, SQL, Kotlin, JavaScript, TypeScript, ReactJS, Node.js, AWS, S3 and DynamoDB, achieving a 20% improvement in tenant engagement through enhanced usability.
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Implemented Content Appropriateness policies for Books using Java, Python, Kotlin, TypeScript, AWS Step Functions and Lambdas backed by AWS CloudFormation Stacks, resulting in an increase in automation rate of policy execution to 96%.
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Designed and implemented a versioning mechanism to enhance the integrity of facts within policies, contributing to a more robust and scalable content-risk management system, resulting in a 25% reduction in operational costs and enhanced the overall system efficiency.
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Participated in on-call responsibilities and conducted a successful Operational Readiness Review. This meticulous approach ensured smooth event operations and significantly enhanced customer satisfaction, achieving a 40% reduction in response latency.
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Authored and presented a comprehensive system implementation document to senior engineers, effectively communicated complex technical concepts and also addressed the questions.
Identified and analyzed 125 Ground Operations injuries and 80 instances of aircraft wing overlaps using Power BI and Tableau.
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Forecasted aircraft damage costs with 95% accuracy using Python, SQL, Data Visualization techniques and Machine Learning models.
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Predicted a 25% reduction in overhead bin injuries in an aircraft using Predictive Models and Machine Learning techniques, resulting in improved safety measures.
