DevOps Strategy for AI Deployment

I’m looking for a seasoned DevOps / Cloud engineer to guide and implement a future-proof deployment approach for an AI-enabled application that is still taking shape. The immediate goal is to evaluate managed cloud services against a self-hosted setup, balancing cost, scalability, reliability, and the possibility of GPU workloads as they become necessary.

What I need from you
• A comparative analysis of the major cloud providers versus an on-prem or hybrid model, highlighting trade-offs in pricing, performance, security, and operational overhead.
• A containerization blueprint built around Docker that can be promoted unchanged from local development through production.
• A CI/CD pipeline designed and delivered as code, with tool selection left open—recommend what fits best for automated testing, security scanning, and zero-downtime releases.
• Deployment automation using infrastructure-as-code so the stack can be reproduced on demand and evolved without manual tweaks.
• Clear architectural documentation, diagrams, and runbooks so the team can take over day-to-day operations once the foundation is in place.

Acceptance criteria
1. End-to-end pipeline running from repository commit to deployment in a staging environment.
2. One-click or single-command promotion from staging to production.
3. Infrastructure definitions stored in version control and capable of spinning up identical environments.
4. Documentation detailing decisions, cost estimates, and upgrade paths for GPU support or self-hosted model serving.

If you thrive on designing robust, flexible cloud architectures and enjoy mentoring teams through modern DevOps practices, I’m ready to get started.