Machine Learning, Computer Science, Cloud Computing, Comouter networks

Prameela . Prakasha Kubsad

Irvine, California ***17 +1-720-***-**** ********@***.*** linkedin.com/in/prameelapk EDUCATION

University Of California, Irvine, California (3.767/4.0) Master of Computer Science, Expected Dec 2025 (Courses:Data Management, AI, Machine Learning, Operating Systems) KLE Technological University, Karnataka, India(3.608/4.0) B.E, Computer Science, 2021(Courses: Statistical Modeling, Computer Networks, OS, Principles of Compilers) TECHNICAL SKILLS

• Languages : C, C++, Python, Java, CSS

• Operating Systems : Debian, Ubuntu, CentOS, UNIX/Unix

• Cloud Computing: Linode, GCP, AWS

• Routing Protocols : BGP, OSPF, IS-IS, EIGRP

• Frameworks : React.js, Angular, Express, Node.js

• Scripting : JavaScript, HTML, Shell

• Devtools : Code Blocks, Eclipse, Wireshark, Jupyter Notebook, Spyder, Visual Studio Code EXPERIENCE

Akamai Technologies Jul 2023 – Aug 2024

Cloud Support Engineer (Frameworks : Edge DNS, Net Storage, Media Live Streaming) Remote – Bangalore, Karnataka, India

• Hands on experience troubleshooting a wide range of Akamai's Delivery products, with expertise in Media solutions such as Storage, Live Streaming, Cloud Wrapper, and Edge DNS, ensuring seamless content.

• Monitored and optimized 30+ customer configurations and high-traffic Go-live events, including the FIFA World Cup and IPL 2023, successfully managing dynamic data to ensure uninterrupted video streaming, leading to a 40% increase in customer

• Conducted in-depth network analysis using TCP traceroutes, packet captures, and Wireshark to diagnose and resolve issues at the TCP/IP and application layers, reducing average resolution time by 35%.

• Implemented automation solutions that streamlined operational processes, improving efficiency by 25%, reducing manual effort by 30%, and enhancing overall experience. Technical Solution Engineer (Frameworks : TCP/IP, Load Balance, WireShark, DNS) Jul 2022 – Jun 2023

• Collaborated with cross-functional teams, including Security Architects, Engineering, Operations, Sales, Professional Services, and Account Management, to resolve complex technical issues, reducing average resolution time by 25%.

• Optimized client web services performance by 30% through DNS-level and Application Layer Load Balancing techniques, leveraging cookies, headers, paths, and variables for efficient traffic distribution. Technical Solution Engineer Associate (Frameworks : Splunk, Web Application Firewall) Jul 2021 – May 2022

• Independently worked with 10+ customer technical support teams on post-sales technical issues, conducting complex data analysis across distributed networks and enhancing origin infrastructure interactions, leading to a 20% improvement in issue resolution efficiency.

• Monitored and analyzed Security Monitor dashboards and Splunk traffic logs, proactively detecting and mitigating potential network security threats, ensuring 100% compliance with security protocols and escalating critical issues to the appropriate teams.

ACADEMIC PROJECTS

Music Recommendation System Oct 2024 – Dec 2024

• Developed a Music Recommendation System using the Spotify dataset from Kaggle, leveraging Gaussian Mixture Model

(GMM) to cluster songs based on features like danceability, genre, mode, and liveliness for personalized recommendations.

• Designed an optimized selection strategy, recommending more songs from clusters with higher silhouette scores to enhance recommendation accuracy and user experience.

Energy Efficient VM Management in OpenStack Jan 2021 – Jun 2021

• Goal of the project was to predict the load on each host using predictive algorithms and to schedule the VMs as per user request on private cloud called OpenStack.

• The outcome of the project was measured by the Efficiency obtained by performing load balancing the income requests which raised the efficiency by 15%.

Efficient Usage of Compute Nodes in Cloud Environment Aug 2020 – Dec 2020

• The aim of the project was to analyze host resource utilization to predict the host state.

• Implemented Scheduling algorithms for allocation of request VMs using Machine learning principles.

• Prediction of correct host state was improved by 85% and resource utilization efficiency spiked to 83%.

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