About
Highly motivated Software Engineer with a strong foundation in full-stack development, AI/ML, and DevOps, demonstrated by quantifiable achievements in system optimization and automation. Proven ability to enhance system reliability by 40%, reduce data retrieval time by 65%, and decrease manual effort by 80%. Seeking to leverage expertise in robust system architecture, scalable solutions, and cutting-edge technologies to drive innovation and efficiency in a dynamic tech environment.
Work
Hyderabad, Telangana, India
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Summary
Software Engineer at Dell Technologies optimizing data retrieval, migrating services, enhancing system reliability, and streamlining deployment processes for critical applications.
Highlights
Optimized data retrieval time by 65% from a Vector DB by developing a learning agent that leverages the LLaMA model and knowledge graphs for semantic embedding clustering.
Migrated critical Spring Boot services from MongoDB to Oracle DB, while concurrently developing and maintaining robust RESTful APIs to ensure seamless data operations.
Enhanced system reliability by 40% through the implementation of a unified logging framework, comprehensive monitoring dashboards, and auto-healing mechanisms via Splunk alerts and Dynatrace.
Ensured smooth and automated deployment of applications on Kubernetes by integrating and optimizing CI/CD pipelines, significantly improving operational efficiency.
Hyderabad, Telangana, India
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Summary
Software Engineer Intern focused on automating development pipelines, improving deployment reliability, and enhancing microservice security within Dell Technologies.
Highlights
Reduced manual effort by 80% in GitLab pipelines by building a FastAPI service to automate the setting of latest Docker images.
Improved deployment reliability by automating the validation of over 100 Airflow instances using a Bash script within CI/CD pipelines to verify Kubernetes pod status.
Increased DevOps score from 75% to 92% by proactively identifying and remediating microservice vulnerabilities using Snyk scans.
Implemented comprehensive unit tests to ensure high code coverage and integrated results with SonarQube for continuous quality analysis.
Kent Ridge, Singapore, Singapore
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Summary
Research Intern focused on data analysis, machine learning, and cybersecurity, specifically analyzing and enhancing CAPTCHA security.
Highlights
Conducted in-depth research into data analysis and data mining, including machine learning concepts such as regression, classification, clustering, and neural networks.
Analyzed CAPTCHA vulnerabilities using CNN and TensorFlow (Keras) to inform the development of robust CAPTCHA solutions, enhancing security for business teams.
Published research findings on CAPTCHA vulnerabilities and presented robust solutions in an IEEE Explore paper, contributing to academic discourse on cybersecurity.