RUCHIK ROHIT DAMA

ad0aqn@r.postjobfree.com +1-812-***-**** LinkedIn GitHub

Professional Experience

Software Development Engineer Amazon Pharmacy Sept 2022- Present

● Executed a robust data processing pipeline, rigorously tested across Beta, Gamma, and Prod environments.

● Automated IaaS-backed lambda functions and project setup, seamlessly integrating TestNG, JUnit 5, and Mockito for effective testing. Streamlined through a robust CI/CD pipeline, ensuring secure, continuous, and efficient deployments.

● Implemented Carnival and rollback alarms for auto-rollback on triggered alarms, ensuring robust pipeline integrity.

● Enhanced overall system security by skillfully crafting and rigorously enforcing Web Application Firewall (WAF) rules across multiple APIs managed using Ruby on Rails, effectively mitigating potential threats.

● Demonstrated proficiency in effectively leveraging Docker for application deployment, extensive API testing, and seamless deployment of website updates, ensuring efficient and reliable web services management.

● Enhanced Amazon Pharmacy Search with dynamic widgets integrated using Java, JavaScript, React. Java handled backend processing and data sourcing, JavaScript and React boosted frontend interactivity, improving user engagement.

● Contributed to the Amazon Pharmacy iOS app team's project, developing a label scanning feature for prescription data extraction, involving OCR, text classification, performance optimization, and HIPAA compliance.

● Leveraged hydra canary testing to adapt data strategies for evolving market dynamics, troubleshooting challenges and ensuring competitiveness and relevance in the dynamic pharmaceutical landscape through updated data logic.

● Efficiently managed on-call duties, swiftly addressing tickets and bug fixing, troubleshooting, and collaborating on pipeline monitoring dashboards, showcasing our shared commitment to system reliability and performance. Research Assistant Indiana University (Prof. Sagar Samtani) Nov 2021 – July 2022

● Crafted a bipartite graph for visualizing the correlation between conventional source code vulnerability scanners and their application to Machine Learning and Deep Learning vulnerabilities.

● Assessed vulnerabilities in AI repositories, utilizing GraphSAGE graph embedding for comprehensive analysis.

● Developed graph-based clustering to categorize similar vulnerability repositories, revealing patterns and correlations in AI code, leading to insightful vulnerability mitigation with a remarkable silhouette score of 0.92.

● Transformed repository insights into data-driven conclusions, guiding strategic decisions. Presented cluster implications for enhancing security and minimizing production risks. Notably, this work was published in ICDM MLC, 2022 Research Assistant Indiana University (Prof. Pettus Walter) May 2021- Aug 2021

● Optimized PostgreSQL query performance, indexing and reorganizing 5 years of neutrino mass data from 200 sensors, cutting critical query runtime from 10-12 minutes to 10-15 seconds.

● Devised and implemented a data management procedure, utilizing the pg-cron extension in PostgreSQL, to systematically archive and remove redundant data on a bi-weekly basis. Publications

Identifying Patterns of Vulnerability Incidence in Foundational Machine Learning Repositories on GitHub: An Unsupervised Graph Embedding Approach ICDM MLC, 2022

Education

Indiana University, Bloomington August 2022

Master of Science, Data Science GPA: 3.57

Courses: Advance Database, Machine Learning, Artificial Intelligence, Network Science, Statistics University of Mumbai, Mumbai, India October 2020

Bachelor of Engineering, Computer Engineering GPA: 3.90 Courses: Databases, Data Structures, Applied Algorithms, Natural Language Processing Projects

Ticket Checker Android Project Android Studio, Java, Firestore, XML

● Developed an Android Application using Android Studio and Firebase for Ticket Checkers in India to automate the ticketing tasks using QR code and messaging systems. Cancer Drug Response Prediction SVR, RBM-based autoencoders

● Implemented Support Vector Regression (SVR) models in conjunction with RBM-based autoencoders, Achieved a remarkable 0.79 Pearson correlation in the analysis of cancer cell responses to drugs. Technical Skills

Programming Languages Python, R, Java, C, C++, Javascript, Ruby, React, Typescript, NodeJS, SQL Database and Web Technologies PostgreSQL, MySQL, HTML5, CSS, JSON, REST APIs Machine Learning Regression, Classification, Clustering, Ensemble models Tools Tableau, Matplotlib, Github, Jira, Hydra, Junit5, TestNG, Maven, Jenkins, Docker AWS Services Lambda, DynamoDB, Kinesis, State Functions, WAF, Athena, S3, Microservices

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