Cloud Architecture & Devops

Well-Designed Cloud Architecture Is the Difference Between Infrastructure That Scales and Infrastructure That Surprises You.

• AWS architecture design and implementation: Designing and implementing AWS infrastructure architectures that are appropriately sized, correctly configured for high availability, and structured to support the security and compliance requirements of the applications they host -with VPC design, subnet architecture, IAM policy design, and the security group configuration that defence-in-depth requires
• Infrastructure as Code (IaC): Defining all infrastructure in code -using Terraform, AWS CloudFormation, or AWS CDK -so infrastructure changes are version-controlled, peer-reviewed, tested in non-production environments, and fully reversible. Infrastructure-as-code eliminates the configuration drift and undocumented change accumulation that manually managed infrastructure inevitably develops
• Multi-region and high availability architecture: Designing the high availability architecture that allows applications to survive the failure of individual components -multi-availability-zone deployments, load balancer configuration, database replication, and the failover logic that minimises the impact of infrastructure failures on application availability
• Database architecture and management: Designing and managing the database infrastructure -RDS configuration for relational databases, ElastiCache for caching, DynamoDB for high-throughput key-value workloads, and the backup, replication, and performance optimisation that production database management requires
• Content delivery and CDN architecture: Implementing CloudFront CDN configuration that delivers static assets and, where appropriate, cached dynamic content from edge locations close to users -reducing latency, reducing origin server load, and providing the DDoS protection that CDN distribution delivers as a byproduct of the architecture
• Cloud cost optimisation and FinOps: Analysing cloud spending patterns, right-sizing compute resources, implementing Reserved Instance and Savings Plan coverage for predictable workloads, and establishing the FinOps practices that make cloud costs visible, predictable, and aligned with the business value being delivered

Docker and Kubernetes Are Not Buzzwords. They Are the Answer to Environment Inconsistency and Deployment Complexity.

• Docker containerisation: Containerising applications using Docker -designing the Dockerfile architecture, implementing multi-stage builds that produce minimal, secure production images, establishing the image versioning and registry management practices that production container operations require, and implementing the container security scanning that prevents vulnerable images from reaching production
• Kubernetes cluster design and implementation: Designing and implementing Kubernetes clusters on Amazon EKS -cluster architecture, node group configuration, namespace design, resource quota management, and the RBAC policies that control access to cluster resources with the granularity that enterprise security requires
• Kubernetes deployment and service management: Defining the Kubernetes deployment manifests, services, ingress configurations, and ConfigMaps that manage application deployments in the cluster -with rolling update strategies that achieve zero-downtime deployments, health checks that prevent unhealthy pods from receiving traffic, and resource limits that prevent individual workloads from affecting cluster stability
• Horizontal and vertical scaling: Implementing the auto-scaling configuration that adjusts compute capacity in response to application load -Horizontal Pod Autoscaler for pod-level scaling, Cluster Autoscaler for node-level scaling, and Vertical Pod Autoscaler for resource optimisation -ensuring the application scales to meet demand without over-provisioning during low-load periods
• Service mesh and inter-service communication: Implementing service mesh architecture for complex microservices environments -managing service-to-service communication, implementing mutual TLS for inter-service encryption, load balancing between service instances, and the observability instrumentation that makes microservices architectures debuggable in production
• Container security and compliance: Implementing the container security practices that enterprise applications require -image vulnerability scanning in the CI/CD pipeline, runtime security monitoring, pod security policies, secret management using AWS Secrets Manager or HashiCorp Vault, and the compliance controls that regulated industries require in containerised environments

A Good CI/CD Pipeline Is Not a Luxury. It Is the Infrastructure That Makes Reliable, Frequent Deployment Possible.

• CI/CD pipeline design and implementation: Designing and implementing CI/CD pipelines using GitHub Actions, GitLab CI, AWS CodePipeline, or Jenkins -with the build, test, security scan, and deployment stages that automate the delivery process and eliminate manual steps that introduce error and delay
• Automated testing integration: Integrating automated test suites into the CI pipeline -unit tests, integration tests, API contract tests, and end-to-end tests -with quality gates that prevent code that does not pass the test suite from advancing through the pipeline, and test result reporting that makes failures immediately visible to the engineering team
• Security scanning and SAST/DAST integration: Integrating static application security testing and dependency vulnerability scanning into the CI pipeline -so security issues are caught at the point of code commit rather than discovered in production, and so known-vulnerable dependencies are flagged before they reach the deployment pipeline
• Environment management and promotion: Designing and managing the environment promotion workflow -from developer environment through integration, staging, and production -with environment-specific configuration management, database migration automation, and the promotion gates that ensure each environment reflects a well-tested, correctly configured version of the application
• Monitoring, alerting and observability: Implementing the monitoring and observability stack that gives engineering and operations teams visibility of application behaviour in production -application performance monitoring with AWS CloudWatch or Datadog, structured logging with centralised log aggregation, distributed tracing for request-level visibility, and the alerting configuration that ensures the right people are notified of the right events at the right time
• DevOps practices and team enablement: Working with engineering teams to adopt and embed the DevOps practices that sustain delivery velocity beyond the initial infrastructure implementation -on-call runbook development, incident management process design, blameless post-mortem culture, and the engineering practices that prevent the same classes of incident from recurring