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Kamran Code

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DevOps, Hosting & Deployment

DevOps, hosting and deployment for web applications and AI systems: Railway and container deployments, GitHub Actions pipelines, domains and DNS, TLS and security headers, object storage, migrations, monitoring and runbooks.

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Software isn't finished when it works on a laptop. It needs repeatable builds, environments, deployments that can be rolled back, migrations that run before traffic arrives, a domain that resolves with a valid certificate, and monitoring that tells you before customers do.

I set up pragmatic production infrastructure — multi-service Railway projects declared as code, containerised applications, GitHub Actions pipelines, DNS and email authentication done properly, and observability sized for the business, not for a hypothetical scale. For AI features this extends to serving models, evaluation passes and cost controls.

Typical use cases for DevOps, hosting & deployment

  • Taking a project live on Railway with web, API, worker, database and storage services
  • Containerising an existing application and its local development stack
  • CI/CD pipelines that typecheck, test, build and deploy on every push
  • Custom domains, DNS, TLS, SPF/DMARC and security headers
  • Monitoring, healthchecks, log retention and incident runbooks
  • Serving open models and running evaluation and cost controls for AI features

What a DevOps, hosting & deployment engagement delivers

  1. 01Railway project (or equivalent) with services, environments, variables and pre-deploy migrations — as code where the platform allows
  2. 02Dockerfiles and Compose stack for local development
  3. 03GitHub Actions workflows with validation before deploy
  4. 04Domain and DNS setup with certificate, redirects and email-authentication records
  5. 05Security headers, least-privilege database roles and a secrets audit
  6. 06Healthchecks, logging, backups and a plain-language runbook

How a DevOps, hosting & deployment engagement runs

  1. 01Understand

    Who uses the system, what they need to do, what the current process looks like and what actually has to change.

  2. 02Architect

    Data model, boundaries, integrations, where AI genuinely helps and where it doesn't — decided before code is written.

  3. 03Build

    Backend, frontend and data layers built incrementally, with working software reviewed early rather than a big reveal at the end.

  4. 04Integrate

    Payment providers, email, CRMs, model providers and internal services connected with clear contracts and error handling.

  5. 05Deploy

    Containers, CI/CD, environments, backups and monitoring so the system is operable — not just running.

  6. 06Improve

    Usage, performance and errors observed after launch and fed back into the next iteration.

Have a project in mind?

Tell me what you're trying to build. I'll come back with an honest view on scope, architecture and the right approach.

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