AI-Integrated DevOps & Platform Engineering
Turn model experiments into reliable, observable production systems. We build the platform layer that AI teams need to ship safely: GPU scheduling, model serving, CI/CD, and monitoring.
How it works
From model artifact to production: the platform path we engineer and operate.
What we build
- GPU cluster & inference platform setup on Kubernetes
- Model serving with vLLM, Triton, or KServe
- CI/CD and GitOps for model promotion (dev → staging → prod)
- Observability for latency, GPU utilization, cost, and drift
- Secrets, networking, RBAC, and security hardening
Scope
We handle
- Kubernetes platform design & operations
- Inference serving infrastructure
- CI/CD, GitOps, and environment promotion
- Monitoring, alerting, and runbooks
Better with a partner
- Model training / research
- Application & product UI
Typical engagement
A phased path from assessment to production, with an optional retainer for ongoing operations.
- 01
Platform assessment
Review your current GPU and serving setup, CI/CD gaps, and observability. Deliver a prioritized roadmap.
- 02
Foundation build
Stand up Kubernetes, inference serving (vLLM, Triton, or KServe), and GitOps promotion paths.
- 03
Production hardening
SLOs, alerting, cost controls, secrets and RBAC, and rollback runbooks.
- 04
Operate & improve
Optional retainer for capacity planning, model promotion support, and ongoing platform evolution.
Ready to take your AI workloads to production?
Let's talk about your platform: cloud, hybrid, or edge. Start with a short, no-pressure conversation.