Hi, I'm Gautham Gali, a Full Stack Java developer and DevOps engineer with a passion for building cloud-native, scalable web applications using Java, Spring, and modern backend technologies. I specialize in automating infrastructure with CI/CD pipelines, Docker, Kubernetes, and infrastructure-as-code tools to deliver robust and efficient solutions.
(March 2025 - Present)
Website: gmu.edu
Conducting research on Vision Transformers (Google), Swin Transformers (Microsoft), and Hyperspectral Transformers for biometric authentication. Developed a high-accuracy authentication system using Transformer-based architectures, improving image classification and reducing false positives.
(August 2023 - December 2024)
Website: vt.edu
Working on AI-driven automation and healthcare solutions, focusing on web scraping, LLMs, and deep learning models. Developed intelligent systems to streamline research workflows, manage databases, and enhance patient diagnostics using AI.
Automated research article extraction from Sage Journals, saving 200+ hours of manual work.
Developed an AI-based GI disease detection system using deep learning models on KVASIR dataset.
(March 2021 - July 2023)
Website: informatica.com
Worked on cloud automation, CI/CD, and Kubernetes infrastructure, improving deployment efficiency and system scalability. Led efforts in multi-cloud optimization, predictive analytics, and cost-efficient DevOps strategies, resulting in 85% faster deployments and $2M cost savings.
Led OpsInsight & IICS cloud automation, optimizing cost, reliability, and scalability.
Developed OpsInsight monitoring system, ensuring 99.999% system uptime.
(February 2020 - Feb 2021)
Website: nokia.com
Worked on Kubernetes orchestration, Python-based server optimizations, and database management, contributing to improved uptime, feature delivery, and issue resolution.
(April 2018 - February 2020)
Website: orbysol.com
Contributed to building cloud-native infrastructure for data platforms using AWS and Terraform. Focused on automation, reliability, and observability of containerized data ingestion systems.