Jun 2024 – Aug 2024 · 2 mos · Basking Ridge, New Jersey, United States · Hybrid

• Architected a cloud-native, real-time network optimization platform processing 10TB+ daily data from 100,000+ cell towers using Apache Kafka, Spark, and AWS services
• Developed and deployed an ensemble of ML models (Random Forest, XGBoost, and Deep Neural Networks) on Kubernetes to forecast optimal cell site locations, improving network coverage predictions by 28%
• Engineered scalable ETL pipelines using Apache Airflow, integrating data from multiple sources (IoT sensors, user metrics, geographical data) for comprehensive network analysis
• Implemented a custom AutoML solution using Ray and MLflow, automating model selection and hyperparameter tuning, reducing model development time by 40%
• Created an interactive data visualization dashboard with React and D3.js, providing real-time insights into network performance and expansion opportunities

May 2022 – Aug 2022 · 3 mos · Austin, Texas, United States

• Assisted a team of DevOps engineers by building a full-stack web application that tracked the status of over 150 applications and their dependencies, allowing for up-to-date information about the state of application stacks.
• Developed a robust REST-API endpoint for the backend using Java, Groovy, and Spring Boot, enabling seamless data transfer to the front end and enhancing overall application efficiency and performance.
• Built and implemented front-end components using React and JavaScript and integrated them with the back end using React Redux.
• Collaborated within an AGILE/Scrum framework, using Bitbucket and Git to manage code repositories and a Jira board to monitor progress, prioritize tasks, and create user stories with the team.
• Wrote Unit tests for all code, ensuring a minimum of 90% code coverage for high-quality, maintainable code