# Kubernetes engineer — since 2020

> Running, scaling and restarting containers across several machines with Kubernetes, model serving and inference workloads included.

- Highlights: 6 years
- Experience: Since 2020
- Updated: 2026-11-08
- Source: https://muhammetsafak.com/expertise/kubernetes/
- Language: en-US
- Author: Muhammet Şafak

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## Me in the Kubernetes ecosystem

For me Kubernetes is the answer to how an application packaged with [Docker](https://muhammetsafak.com/expertise/docker/) stays up across several machines: where it runs, how many copies there are and what happens when one goes down are read from the declared state. The role side of this work is on the [DevOps and infrastructure page](https://muhammetsafak.com/expertise/devops/).

Kubernetes sits where two roles meet. On the infrastructure side it is the layer that manages containers on top of Linux servers; on the AI systems side it is the platform where I deploy and scale model serving and inference workloads as containers. I cover that side separately on the [AI Systems page](https://muhammetsafak.com/expertise/ai-systems/).

What Kubernetes solves is scaling and restarting; how the application is packaged is a question for the [Docker page](https://muhammetsafak.com/expertise/docker/).

## What is the Kubernetes Engineer role?

Kubernetes is a portable, extensible, open source platform for managing containerized workloads and services. It takes care of scaling and of restarting containers when they fail. A Kubernetes engineer is the person who sets up and operates this layer the containers run on.

## What does the Kubernetes Engineer do?

I use Kubernetes as the orchestration layer where I run, scale and restart containers across machines. Model serving and inference workloads are part of that: I serve trained models behind an API with NVIDIA Dynamo-Triton. The application arrives as a [Docker](https://muhammetsafak.com/expertise/docker/) image.

## Technologies I pair it with

- Kubernetes: The orchestration layer where I run, scale and restart containers across machines, model serving and inference workloads included.
- Docker: Development environment and deployment unit.
- Linux: The server operating system applications run on.
- Terraform: For describing infrastructure in code and setting it up repeatably.
- GitHub Actions: The CI/CD pipeline that defines test and deploy steps in the repository.
- NVIDIA Dynamo-Triton: The model serving server, formerly NVIDIA Triton Inference Server, that I use to put trained models behind an API.

## Questions in this area

- [My containers keep getting OOMKilled—how do I set memory limits correctly for PHP-FPM and Go?](https://muhammetsafak.com/just-ask/containers-oomkilled-set-memory-limits-php-fpm-go/) (2026-10-13)
- [Should I define the healthcheck in the Dockerfile or leave it to Kubernetes probes?](https://muhammetsafak.com/just-ask/should-i-define-the-healthcheck-in-the-dockerfile-or-leave-it/) (2026-07-21)
- [Health check design: how do I separate Liveness and Readiness?](https://muhammetsafak.com/just-ask/health-check-design-liveness-vs-readiness/) (2026-06-06)

## Frequently asked

### How long have you worked with Kubernetes?

Since 2020 — 6 years.

### What is Kubernetes?

Kubernetes is a portable, extensible, open source platform for managing containerized workloads and services. It provides a framework to run distributed systems resiliently and takes care of scaling and failover.

### What is Kubernetes used for?

According to the Kubernetes documentation it provides service discovery and load balancing, automated rollouts and rollbacks, resource placement, restarting failed containers and horizontal scaling. I use it to run, scale and restart containers across several machines.

### What is the difference between Docker and Kubernetes?

Docker packages the application and its environment as a container; Kubernetes manages running, scaling and restarting those containers across several machines. The first is a packaging question, the second an orchestration question.

### What workloads do you run on Kubernetes?

Applications packaged as containers, plus model serving and inference workloads. I use NVIDIA Dynamo-Triton to serve trained models behind an API.

### What is the difference between Kubernetes and Terraform?

Kubernetes runs, scales and restarts containers; Terraform is for describing infrastructure in code and setting it up repeatably. They answer different questions and can be used together.

### Where should you start learning Kubernetes?

Kubernetes manages containers, so you first need to understand what a container is. The Docker page and the Docker guide on the blog are a good first step for that reason.