REMYA G R

DevOps Engineer with 8+ years of experience in automating deployment pipelines, optimizing infrastructure, and enhancing software delivery. Experienced in Kubernetes, CI/CD, Terraform, and cloud platforms like Azure, AWS, and Google Cloud. Skilled in Python scripting, AI, and Machine Learning, with expertise in integrating AI solutions into scalable DevOps workflows.

• AZ-900: Microsoft Azure Fundamentals, Microsoft
• AZ-104: Microsoft Azure Administrator, Microsoft
• Applied AI with Deep Learning, IBM Watson IoT Data science
• Google Cloud Platform Big Data and Machine Learning Fundamentals
• Introduction to Data Science in Python, University of Michigan and offered through Coursera

• Participated in Microsoft Ignite hosted by Microsoft.
• Participation in conference conducted by NCETET.
• Participation in the workshop carried out by Computer Society of India Trivandrum Chapter.
• Participated in Global Impact Challenge by Singularity University and Kerala Startup Mission at Trivandrum Technopark.
• Published a paper in IRJET (A SURVEY ON DIFFERENT COMPRESSION TECHNIQUES FOR EFFICIENT IMAGE TRANSFER).
• Published a paper in IARJSET (HIGHLY SECURE E-T-C SYSTEM VIA ADAPTIVE HISTOGRAM SHIFTING).
• Published a paper in IJRTER (ENCRYPTION THEN COMPRESSION SYSTEM WITH DATA HIDING IN ENCRYPTED IMAGES).

• Date of Birth: 05/26/91
• Nationality: Indian
• Marital Status: Married

Ship Detection Using AI and Deep Learning

Developed an AI-powered system to detect and classify ships from satellite imagery, leveraging deep learning techniques. Designed and implemented a pipeline for data preprocessing, model training, and inference using Python and TensorFlow. Incorporated the solution into a scalable deployment environment using Kubernetes and CI/CD practices, ensuring high availability and performance. The project achieved significant improvements in detection accuracy and operational efficiency, supporting real-time monitoring and decision-making for maritime operations. Proficiently utilized cloud platforms to process large datasets and optimize the solution for cost-effective scalability.

Infrastructure Automation with Terraform for Scalable Web Application

Designed and implemented a fully automated infrastructure provisioning system using Terraform to support a scalable web application. Built modular Terraform configurations to manage resources across AWS, including VPC, EC2 instances, RDS databases, and S3 storage. Integrated the Terraform workflow with CI/CD pipelines to enable automated infrastructure updates and deployments, ensuring seamless rollouts with minimal downtime. Executed state management using remote backend and version control for infrastructure changes, enhancing collaboration and reliability.