I design, automate, secure, and document production-oriented AWS infrastructure.

I'm Simeon Siaka, a cloud infrastructure engineer building hands-on AWS projects around real business and operational requirements.

My work goes beyond creating resources in the AWS console. I focus on:

• designing highly available and fault-tolerant architectures
• provisioning repeatable infrastructure with Terraform
• automating delivery with secure CI/CD workflows
• applying least-privilege IAM and secure secret management
• documenting architecture decisions, validation, and troubleshooting
• balancing reliability, performance, security, and cost

I am currently building an 80-project AWS infrastructure portfolio. Seven projects are complete, with a multi-tenant SaaS platform now in progress.

Designed a serverless-first, multi-AZ logistics architecture capable of absorbing a modeled 50× traffic spike without manual intervention.

Engineering evidence:

• separated spike-prone order intake from the EC2 web tier
• used API Gateway, Lambda, DynamoDB, SQS, and SNS for asynchronous order processing
• documented DynamoDB access patterns before schema design
• defined reserved Lambda concurrency and SQS buffering behavior
• selected RDS MySQL Multi-AZ and Redis according to workload and cost requirements
• documented observability, failure testing, and regional resilience decisions

Stack: AWS · Lambda · API Gateway · DynamoDB · SQS · SNS · RDS · ElastiCache · EC2 · Auto Scaling · CloudWatch

Built a production-oriented three-tier customer management application using Terraform and Docker.

Engineering evidence:

• provisioned private application and database subnets
• deployed Docker images from Amazon ECR to an Auto Scaling Group
• routed traffic through an Application Load Balancer with health checks
• integrated Flask and SQLAlchemy with Amazon RDS MySQL
• replaced hardcoded credentials with AWS Secrets Manager
• added automated Terraform, Bash, and Python validation checks
• documented architecture, deployment flow, engineering decisions, and troubleshooting

Stack: AWS · Terraform · Docker · ECR · ALB · Auto Scaling · RDS · Secrets Manager · Flask

Implemented a secure static website delivery and deployment pipeline without long-lived AWS credentials.

Engineering evidence:

• kept the S3 origin private behind CloudFront Origin Access Control
• automated deployments and CloudFront invalidation with GitHub Actions
• used GitHub OIDC and AWS STS for short-lived deployment credentials
• scoped the deployment role to the required S3 bucket actions
• documented security, cost, Well-Architected alignment, and operational decisions

Stack: Amazon S3 · CloudFront · IAM · AWS STS · GitHub Actions · OIDC

Provisioned a repeatable AWS web tier that distributes traffic and automatically replaces unhealthy instances.

Engineering evidence:

• used Terraform data sources, variables, outputs, and resource references
• configured an ALB, target group, launch template, and Auto Scaling Group
• restricted instance traffic to requests originating from the load balancer
• automated Apache installation with EC2 user data
• validated target health, traffic distribution, and instance replacement

Stack: Terraform · EC2 · ALB · Auto Scaling · Linux · Bash

Building the authentication, tenant-isolation, application, and infrastructure foundations for a multi-tenant SaaS platform. The project is being developed through feature branches and pull requests, with architecture and security documentation added alongside implementation.

Current focus: Amazon Cognito · OIDC/OAuth 2.0 · tenant isolation · secure session handling · infrastructure automation

AWS services used across projects: VPC, EC2, Auto Scaling, ALB, NLB, RDS, ECR, S3, CloudFront, IAM, STS, Secrets Manager, Lambda, API Gateway, DynamoDB, SQS, SNS, ElastiCache, and CloudWatch.

1. Start with the business problem and measurable requirements.
2. Design the architecture and document important trade-offs.
3. Provision repeatable infrastructure instead of relying only on console steps.
4. Apply security controls such as private networking, least privilege, OIDC, and managed secrets.
5. Validate the deployment through health checks, failure testing, and observable outcomes.
6. Record troubleshooting lessons so the repository explains both the solution and the engineering process.

• multi-tenant SaaS architecture and tenant isolation
• Amazon Cognito authentication and authorization
• container orchestration with Amazon ECS
• CloudWatch monitoring, alarms, and operational dashboards
• automated infrastructure security and quality checks
• Kubernetes, Prometheus, and Grafana as upcoming portfolio areas

Building secure, scalable, automated, and well-documented cloud infrastructure.