AWS Solutions for DevOps & Platform Engineers

For DevOps and Platform Engineers

As a DevOps or platform engineer, you own the platform that every other team ships on. Your job: automate the toil, enable developers to deploy in under 10 minutes, build reliability into the defaults, and do it all without becoming a ticket queue. In 2026, that platform increasingly includes AI-assisted development (Amazon Q Developer, Kiro IDE), EKS Auto Mode as the default managed-Kubernetes baseline, supply-chain security as a compliance requirement rather than a nice-to-have, and OpenTelemetry-stable observability replacing siloed vendor stacks. AWS gives you the building blocks; platform engineering is the practice of assembling them into paved roads.

Your Challenges

Challenge 1: CI/CD Pipeline Reliability & Speed

• Build times drift past 10 minutes; developers context-switch, PRs stack up, and the pipeline becomes a bottleneck everyone complains about.
• OIDC-based keyless authentication from GitHub Actions to AWS is now the standard — no long-lived access keys, short-lived STS credentials per run — but legacy pipelines still use IAM users.
• Blue-green, canary, and feature-flagged deploys require disciplined traffic management with ALB, ECS service update strategies, or Lambda weighted aliases.
• You need: fast feedback loops, credential-free pipelines, and automated rollback wired to SLO burn or CloudWatch alarms.

Challenge 2: Container Orchestration & Node Efficiency

• EKS node group management — version upgrades, security patches, resource-request tuning — used to eat a week every quarter; Auto Mode largely removed it.
• When you do run self-managed Karpenter, bin-packing, Spot integration, and Graviton4 node pools deliver 30–50% compute cost reductions.
• Service mesh decisions (App Mesh deprecated, VPC Lattice, Istio, Linkerd, Cilium service mesh) need clear trade-off analysis — the landscape shifted in the last 18 months.
• You need: right-sized compute, clear policy on when Auto Mode vs self-managed wins, and simplified workload networking.

Challenge 3: Observability at Scale

• Logs, metrics, and traces are siloed across CloudWatch, X-Ray, and third-party tools; correlation requires manual effort during incidents.
• Alert storms from poorly tuned thresholds cause runbook decay and on-call burnout.
• OpenTelemetry 1.0 semantic conventions are stable; AWS Distro for OpenTelemetry (ADOT) and Application Signals provide SLO-based alerting — but adopting them well requires schema discipline.
• eBPF observability (Cilium Hubble, Pixie) fills gaps sidecar-based tooling misses — kernel-level visibility without code changes.
• You need: unified observability, meaningful SLO/SLA tracking, cost-optimized log retention, and alerts that only fire when they should.

Challenge 4: Infrastructure as Code Governance

• Terraform, OpenTofu, and CDK modules written in silos; no shared registry or versioning discipline.
• CDK Toolkit v2 has matured into a first-class authoring and testing experience; OpenTofu is now a credible Terraform alternative for orgs wary of license changes.
• No workflow for peer review; infrastructure changes bypass scrutiny, and drift goes undetected.
• You need: a module registry, automated policy-as-code testing, safe multi-environment promotion, and drift detection wired to alerts.

Challenge 5: Supply-Chain Security

• Every signed image, every SBOM, every provenance attestation is now table stakes for regulated customers and increasingly for all enterprise sales.
• Amazon Inspector generates SBOMs on ECR push; AWS Signer handles Lambda code signing; Sigstore/cosign covers container signing with transparent logs.
• Without a signed-artifact policy enforced in admission, the chain is decorative.
• You need: provenance from commit to runtime, verified at admission, and documented against SLSA levels.

How FactualMinds Helps DevOps Engineers

CI/CD Pipeline Architecture

• GitHub Actions with OIDC keyless AWS authentication — zero long-lived access keys anywhere in the pipeline.
• CodeBuild for language-specific build optimization; multi-stage Docker builds for minimal image size and cache-friendly layers.
• Deployment strategy design: blue-green with ALB target-group switching, canary with Route 53 weighted routing, automated rollback via CloudWatch alarms or Application Signals SLO burn.
• Amazon Q Developer integration for AI-assisted code review, infrastructure generation, and operational investigations.
• GitHub Actions Runner Controller (ARC) on EKS for self-hosted runners with fine-grained IAM and network access.
• Pipeline security: Amazon Inspector SBOM on every push, Secrets Manager for runtime credentials, AWS Signer for Lambda, Sigstore/cosign for containers, and verified admission on deploy.

Container Orchestration & EKS Optimization

• EKS Auto Mode as the default baseline for new Kubernetes workloads; self-managed Karpenter for GPU, Graviton4, and highly cost-sensitive fleets.
• Graviton4 (arm64) node pools: up to 40% cost reduction with no application code changes when workloads support arm64.
• Spot-mixed node pools with Karpenter consolidation and interruption handling.
• Network policies via Cilium or AWS VPC CNI with security groups for pods; VPC Lattice for cross-cluster service connectivity when needed.
• Helm chart management, ArgoCD or Flux GitOps patterns for declarative cluster state; cluster upgrades orchestrated through Argo Rollouts.

Observability & Monitoring

• AWS Distro for OpenTelemetry (ADOT) aligned to OpenTelemetry 1.0 stable semantic conventions — vendor-neutral tracing and metrics.
• CloudWatch Application Signals: SLO definition, error-rate and latency tracking, auto-generated service maps.
• Amazon Managed Grafana and Amazon Managed Service for Prometheus for teams standardized on the open-source stack.
• eBPF observability: Cilium Hubble for network flow visibility, Pixie for application-level introspection without sidecars.
• Intelligent alerting: composite alarms, anomaly detection bands, SLO-burn-based paging, and runbooks parseable by Amazon Q.
• Cost-optimized log retention: CloudWatch Logs Insights for recent data, S3 Express One Zone or standard S3 + Athena for long-term analysis.

Infrastructure as Code Best Practices

• Terraform / OpenTofu module registry with semantic versioning and automated tests (native terraform test / tofu test).
• AWS CDK v2 patterns: L2/L3 constructs, CDK Pipelines for self-mutating deployment, CDK assertions for unit tests.
• OPA, Checkov, or Sentinel policy-as-code enforcing organizational rules before plan apply.
• Multi-environment promotion: dev → staging → production with mandatory plan review and policy gates.
• State file strategy: S3 remote backend with DynamoDB locking (or S3 native locking in 2025+), cross-account state access via IAM roles.
• Drift detection via AWS Config and scheduled plan runs with alerting on unexpected changes.

Supply-Chain Security

• Amazon Inspector SBOM generation on every ECR push and every Lambda deployment.
• Sigstore / cosign container signing with transparent-log publication; keyless signing using GitHub Actions OIDC.
• AWS Signer for Lambda code signing, verified by Lambda at deploy time.
• Admission control: Kyverno or Gatekeeper policies that reject unsigned images in production namespaces.
• SLSA level 3 alignment: build provenance from GitHub Actions reusable workflows, stored alongside the artifact.

Featured DevOps Engagements

• Migrating CI/CD from Jenkins to GitHub Actions with OIDC and Sigstore signing for a 60-person engineering org; cut average deploy time from 27 minutes to 8.
• Migrating 11 EKS clusters to EKS Auto Mode plus self-managed Karpenter for GPU workloads; reduced cluster-ops toil by 45% measured in tickets per quarter.
• Deploying Karpenter with Graviton4 Spot nodes on workloads that could not move to Auto Mode — 38% compute cost reduction without code changes.
• Building an OpenTelemetry-based observability platform replacing a dual CloudWatch + Datadog spend; cut vendor cost by 62% while improving trace coverage.
• Designing a Terraform / OpenTofu module library with automated Checkov policy gates and terraform test coverage for 40+ infrastructure patterns.
• Standing up a paved-road Bedrock Agent template with Guardrails, per-agent IAM, and cost instrumentation — reduced first AI feature ship time from 6 weeks to 4 days.

When a DevOps Engagement Is Not the Right Fit

• Pre-platform, pre-product stage. If you are a two-person team still searching for product-market fit, a platform engineering engagement is premature — start with serverless-first patterns in the Startup Founder engagement.
• No time investment from your engineering team. Our best outcomes come from pairing with your engineers. If you need a fully-outsourced build-and-walk-away engagement, you are better served by a large SI.
• Rigidly locked vendor contracts that exclude OIDC or signing. If compliance or procurement won’t allow modern CI/CD primitives, we can advise on the exception path, but we can’t pretend the pipeline is secure while it still uses long-lived keys.