Freelancers using Amazon EC2 in AhmedabadFreelancers using Amazon EC2 in Ahmedabad
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Harshil Yogi's avatar
Harshil Yogi
Ahmedabad, India
AWS DevOps Engineer | Terraform, CI/CD & Automation
AWS DevOps Engineer | Terraform, CI/CD & Automation
Cover image for Designed and implemented an AI-powered
Designed and implemented an AI-powered AWS SRE automation platform to reduce manual infrastructure monitoring and security reporting. The solution included two independent workflows: • AI-powered EC2 monitoring that detects CPU usage patterns, analyzes them with AWS Bedrock, and generates scaling recommendations with an approval-based remediation workflow. • Automated vulnerability reporting using AWS Inspector, Lambda, S3, Glue, Athena, Bedrock, and QuickSight to generate monthly security insights and executive dashboards. Built the infrastructure using Terraform with an event-driven, serverless AWS architecture. The solution improved infrastructure visibility, reduced manual operational effort, accelerated incident detection, and automated security reporting.
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Cover image for AI Content Automation on AWS
AI Content Automation on AWS | GitHub + Slack + Bedrock Description:- I built a production-grade AI content automation system that transforms GitHub commits and Slack conversations into authentic LinkedIn posts. The solution uses AWS, Terraform, ECS Fargate, and EventBridge to automate content generation, approval, and scheduling. - Automated content generation from GitHub and Slack activity - Human-in-the-loop Slack approval workflow - Security filtering to reduce sensitive data exposure - Scheduled content publishing with EventBridge - Production-ready AWS infrastructure managed with Terraform The result is a reliable, scalable automation pipeline that eliminates repetitive content creation while maintaining human control over what gets published. My Role: AWS DevOps Engineer Technologies: AWS ECS · Fargate · Terraform · Docker · CI/CD · EventBridge · GitHub · Slack · AWS Bedrock
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Cover image for I built an AI-powered AWS
I built an AI-powered AWS log investigation system that allows teams to query logs stored in their existing S3 buckets using natural language. The solution uses AWS Lambda, API Gateway, S3, Bedrock, and Terraform to: - Convert natural-language questions into structured log searches - Automatically discover and filter JSON, CSV, JSONL, and compressed log files - Search across multiple S3 buckets without migrating log data - Use Bedrock to summarize results and provide actionable insights - Keep the log-search function private with IAM-based access control The serverless architecture delivers typical queries in 2–4 seconds at approximately $0.001 per query, while keeping log data within the AWS environment. Technologies:- AWS Lambda · API Gateway · S3 · AWS Bedrock · Terraform · IAM · Python
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Cover image for Automated Windows AMI Patching &
Automated Windows AMI Patching & AWS Infrastructure Automation I built an automated AWS pipeline to eliminate the manual process of patching Windows servers and updating the AMI used by an Auto Scaling Group. The solution uses AWS Image Builder, SSM Patch Manager, CloudWatch, EventBridge, Lambda, and CloudFormation to automate the complete patching lifecycle. Automated Windows patch scanning and installation Automated Sysprep and patched AMI creation CloudWatch logging for patching and audit visibility Automatic Launch Template and ASG updates Preserved existing production configuration The infrastructure is deployed through a single CloudFormation template, creating a repeatable and auditable workflow with minimal manual intervention. My Role: AWS DevOps / Cloud Automation Engineer Technologies: AWS Image Builder, SSM Patch Manager, EC2, Auto Scaling, Lambda, EventBridge, CloudWatch, CloudFormation, IAM, Windows Server, PowerShell
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Amazon EC2(1)