How much Python and shell scripting should a DevOps engineer learn?

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Asked By MellowPine47 On

I started as a DevOps intern and recently moved into a full-time role. Most of the Bash and Python automation I need is generated with AI because my company prioritizes completing tasks quickly. As a beginner, I sometimes worry that I should be able to write larger automation scripts completely on my own.

How deeply should I learn Python and shell scripting? Is it acceptable to keep using AI if I can understand, debug, modify, test, and explain the generated code? I'm also hoping to change jobs around May or June next year, so I'd appreciate advice on what skills I should focus on to become a stronger and better-paid candidate.

My current experience includes beginner-to-intermediate AWS and Azure, Linux, Docker, Kubernetes, Terraform, and Jenkins.

3 Answers

Answered By QuietMaple19 On

Shell scripting is still important because operational work happens in terminals and many teams have existing Bash scripts. Python is also worth learning because it is widely used for automation and lets you build more maintainable tools. You should be able to write basic scripts yourself, work with files and APIs, handle errors, and understand common data structures. If you work heavily in a Windows environment, PowerShell may be more valuable than Bash for some tasks. Go can be a useful future addition, but it should come after you have solid fundamentals.

Answered By CopperWren82 On

Yes, learn both, but you don’t need to stop using AI. The important skill is understanding the output: can you read the script, explain each part, debug it, change the logic, and identify when it is unsafe or incorrect? Treat the first AI-generated version as a draft. Test it, add error handling, and consider what happens if a command fails halfway through.

For DevOps, become comfortable with Bash and learn enough Python to create and maintain practical automation. You don’t need to become a full-time Python software engineer. You already have a broad toolset, so focusing on deeper AWS, Linux, containers, Kubernetes, Terraform, and troubleshooting experience may help more than adding another dozen technologies. AI should make you faster, not leave you helpless when its answer is wrong.

Answered By SilverOtter6 On

The real test is not whether you typed every line—it’s whether you can tell when the script is wrong. Take an AI-generated script you used recently and explain every line without looking anything up. If you can’t, that identifies what to study. Build the habit of changing and testing generated code instead of copying it directly.

For your job search, prepare concrete examples of incidents you diagnosed, automation you improved, deployments you supported, and failures you fixed. Interviewers will usually care more about your reasoning, troubleshooting process, and understanding of the infrastructure than whether you can produce a large script from memory.

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