Tech

AI for DevOps Engineers

Automate infrastructure, deployments, and incident response with AI assistance

DevOps engineers use AI to write and review infrastructure-as-code, generate CI/CD pipeline configurations, debug deployment failures from logs, and create runbook documentation that the entire team can follow during incidents. AI reduces the cognitive load of working across multiple cloud providers, container orchestration platforms, and deployment tools simultaneously.

Common challenges AI helps solve

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Writing error-free Terraform or Kubernetes YAML configurations for complex infrastructure

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Diagnosing production incidents quickly from dense log output under time pressure

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Keeping runbooks and operational documentation current as infrastructure evolves

Top use cases for DevOps Engineers

Write a Terraform module

Write a Terraform module for AWS that provisions: a VPC with public and private subnets across 3 availability zones, an ECS Fargate cluster, an Application Load Balancer with HTTPS listener using ACM certificate, a target group pointing to the ECS service on port 3000, and a security group allowing only 443 inbound and all outbound. Use variables for: region, environment name, VPC CIDR, and certificate ARN. Follow AWS and Terraform best practices.

Debug a deployment failure

Here is the output from a failed GitHub Actions deployment pipeline: [paste logs]. The app is a Node.js service deploying to AWS ECS via Docker. Analyze the logs, identify the exact step that failed, explain the root cause, and provide the specific fix needed in either the Dockerfile, the GitHub Actions YAML, or the ECS task definition — whichever is causing the failure.

Write a CI/CD pipeline config

Write a complete GitHub Actions workflow YAML for a Next.js TypeScript application. The pipeline should: run on push to main and on pull requests, install dependencies with npm ci, run TypeScript type checks, run ESLint, run Jest tests with coverage threshold of 80%, build the Docker image, push to AWS ECR, and deploy to ECS Fargate staging on PR merge. Use GitHub secrets for AWS credentials.

Create an incident runbook

Write an incident runbook for a high CPU alert on our Node.js API service running in Kubernetes. Include: alert definition and severity level, first 5 diagnostic commands to run with expected output descriptions, decision tree for common root causes with remediation steps for each, escalation criteria and contact list format, and post-incident review checklist. Make it usable by an on-call engineer who is not familiar with this service.

Review a Kubernetes manifest

Review this Kubernetes Deployment and Service manifest: [paste YAML]. Check for: missing resource limits and requests, inappropriate restart policies, missing health check probes, security context issues like running as root, missing pod disruption budgets, and any labels or annotations that should follow our naming conventions. Provide a corrected version with inline comments explaining each change.