CodeCraft
Summary
Developed a no-code platform for building and training artificial neural networks.
Highly skilled DevOps Engineer and Cloud Architect with a B.Tech. in Computer Science, experienced in optimizing CI/CD pipelines, automating cloud infrastructure, and developing scalable solutions across AWS and Azure. Proven ability to reduce deployment times by 53%, enhance system efficiency by 27%, and achieve 99.99% uptime, driving significant operational improvements and cost savings in dynamic enterprise environments.
DevOps Engineer
Kolkata, West Bengal, India
→
Summary
As a DevOps Engineer, led initiatives to optimize CI/CD pipelines, migrate applications, and build autonomous AI cloud agents for scalable multi-team workloads, significantly improving deployment efficiency and cloud cost optimization.
Highlights
Optimized CI/CD pipelines, reducing QA deployment time by over 53% from 75 minutes to 35 minutes.
Successfully migrated critical enterprise applications from .NET 6 to .NET 10 across diverse multi-environment Azure infrastructures, ensuring seamless operations.
Engineered and implemented a Kubernetes-based real-time data streaming and processing architecture, significantly enhancing scalability and efficiency for multi-team workloads.
Developing an autonomous AI cloud agent to automate infrastructure, streamline cloud migrations, and optimize deployment orchestration, tenant transfers, monitoring, troubleshooting, and cloud cost by leveraging AI capabilities.
Net-DevOps Engineer
Bengaluru, Karnataka, India
→
Summary
As a Net-DevOps Engineer, developed and automated syslog collection and Kafka producer-consumer systems across 350+ sites, enhancing logging, metrics, and global operational monitoring efficiency by 27%.
Highlights
Automated syslog collector configuration for over 350 sites, including 20 critical locations, leveraging Grafana and Promtail for centralized logging and metrics.
Implemented Python automation for Kafka producer-consumer, improving load distribution among nodes and boosting log transfer efficiency by 27% for comprehensive monitoring.
Streamlined monitoring across Maersk's global operations by establishing a single point of contact for logs, enhancing operational visibility and incident response.
Cloud Architect Intern
Bhubaneswar, Odisha, India
→
Summary
As a Cloud Architect Intern, designed and implemented scalable, highly available cloud infrastructure using Terraform for AWS and Azure, automating provisioning and achieving 99.99% uptime with reduced manual intervention.
Highlights
Architected and deployed scalable, highly available cloud infrastructure across AWS and Azure using Terraform, ensuring robust system performance.
Automated cloud infrastructure provisioning and deployment, decreasing manual intervention by 40% and significantly reducing infrastructure-to-deployment time.
Optimized resource allocation across Development, Pre-production, and Production environments, achieving 99.99% annual uptime with less than 1 hour of downtime.
Research Intern
Bengaluru, Karnataka, India
→
Summary
As a Research Intern, collaborated on predicting power consumption of non-smart devices, leveraging data analysis and model optimization to achieve high accuracy.
Highlights
Collaborated with a research team to predict power consumption of non-smart devices by leveraging smart device data, contributing to innovative energy management solutions.
Conducted extensive research on academic papers to identify optimal models and datasets, enhancing research outcomes and predictive accuracy.
Achieved 97.45% prediction accuracy by modifying an existing model (ALC Model), directly contributing to the core objectives of the research project.
→
B.Tech.
Computer Science and Information Technology
Grade: 8.33
Python, Bash, GoLang, Java.
Terraform, Docker, Grafana, Promtail, Keepalived, Nginx, Git, AWS, Azure, Kafka, Git Actions, Redis.
System Design, Networking, OS, Linux, Monitoring, Logging, Alerting, DevOps, SRE.
Debugging, Optimization, Communication, Teamwork.
Summary
Developed a no-code platform for building and training artificial neural networks.
Summary
Detected various DDoS attacks including SYN Flood, SYN-ACK Flood, ICMP Smurf, and Ping of Death.