React Js Node

SHASHANK SACHETI

Baltimore, MD *****************@*****.*** +1-667-***-**** www.linkedin.com/in/shashanksacheti EDUCATIONAL QUALIFICATIONS

University of Maryland, Baltimore County(UMBC), Baltimore, MD (May 2024) MS - Computer Science

SRM Institute of Science and Technology, Kattankulathur, India (May 2019) B.Tech - Computer Science Engineering

TECHNICAL SKILLS

Programming Languages: Ruby, JavaScript, TypeScript, Python, Java, PHP, C, C++, HTML5, CSS3, SQL, JSON, XML Frameworks and Libraries: Ruby on Rails, React.js, Node.js, Express.js, Vue.js, RSpec, GraphQL, Flask, TensorFlow, PyTorch Databases: MySQL, PostgreSQL, Oracle DB, MongoDB, Redis, Elasticsearch Tools and Platforms: AWS, Google Cloud Vision API, Docker, Azure, Kafka, Git, Magento, Linux, CI/CD, Sidekiq, JIRA WORK EXPERIENCE

Clean Origin (E-commerce), USA - Software Engineer (May 2023 - December 2023)

● Developed a user engagement feature, using React.js, Node.js, and Express.js, implementing a button-triggered popup that presented diamond attribute related questions such as shape, cut, color, clarity, and more.

● Utilized user responses to generate algorithm-driven diamond recommendations, achieving a 30% boost in conversion rate. Enhanced customer satisfaction, saved time, and delivered valuable personalized suggestions.

● Developed a content moderation feature, using React.js, Node.js, Express.js, and Google Cloud Vision API for seamless user uploads of product photos and videos, ensuring adherence to guidelines and filtering out inappropriate content.

● Achieved a 40% reduction in manual checks for customer executives, enhancing operational efficiency. This feature served as a promotional strategy, contributing to a notable 25% boost in the order rate and improved user engagement. CaratLane (E-commerce), India - Software Development Engineer - II (June 2019 - July 2022)

● Conceptualized and constructed a responsive and interactive product information web page using Vue.js and Ruby, featuring robust Q&A functionality allowing users to search, add questions, rate content, and apply filters to sort.

● 35% conversion rate increased through this project, elevated accessibility to detailed product information, resolved key challenges faced by the sales team, and elevated customer interactions.

● Built the Ruby on Rails module which facilitates the seamless requesting and tracking of more than 100 barcode transfers simultaneously, providing comprehensive historical data and real-time status updates on the website.

● Achieved a 25% cost reduction by eliminating reliance on third-party tools for in-house management. Improved operational efficiency, optimized internal processes, and ensured efficient delivery of end-to-end barcode transfers.

● Optimized CaratLane store sales iOS app functionality by 25% by developing several REST APIs using Grape-Swagger in Ruby on Rails. Resolved 15+ user-related issues and bugs to enhance system speed, and efficiency, and reduce downtime.

● Led and supervised the ITR team of software developers to ensure seamless communication and knowledge transfer.

● Collaborated with cross-functional teams using AGILE/SCRUM methodologies to architect, develop, unit test, and deliver scalable, high-quality software solutions.

● Implemented a Kafka-based asynchronous job processing system for enhancing system responsiveness and performance.

● Utilized microservices architecture and Docker containers to enhance scalability, reliability, and deployment efficiency. CaratLane, India - Data Warehouse Engineer (March 2019 - April 2019)

● Leveraged DBeaver and Domo to analyze extensive customer datasets, generating strategic reports that enhanced data readability for cross-functional teams and guided informed business decisions. CaratLane, India - Backend-Developer (January 2019 - March 2019)

● Built a "Pick-up-from-store" feature in Ruby on Rails, enhancing customer convenience. This led to a 30% boost in customer satisfaction and a 20% improvement in order fulfillment efficiency. PROJECTS EXPERIENCE

● Predictive Model for Skin Disorders: Developed a machine learning model in Python using KNN, Naive Bayes, Logistic Regression, and Decision Tree algorithms, achieving 98.6% accuracy in diagnosis and treatment recommendations.

● AI Chatbot for UberSupport: Created a chatbot in Python using the Seq2Seq model and NLP, achieving 92% accuracy in resolving customer concerns for Uber and Uber Eats.

Contact this candidate