<?xml version="1.0" encoding="UTF-8"?><rss xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:content="http://purl.org/rss/1.0/modules/content/" xmlns:atom="http://www.w3.org/2005/Atom" version="2.0"><channel><title><![CDATA[AI Engineer in Public]]></title><description><![CDATA[Documenting my journey from AI Developer to AI Ops Engineer.
I write about Linux, Networking, Docker, Kubernetes, MLOps, AI Agents, RAG systems, and production ]]></description><link>https://ai-engineer-in-public.hashnode.dev</link><image><url>https://cdn.hashnode.com/uploads/logos/6a3778f564f7308e4010e42e/72104191-ff49-4ae0-95fa-5ce34257cfb7.jpg</url><title>AI Engineer in Public</title><link>https://ai-engineer-in-public.hashnode.dev</link></image><generator>RSS for Node</generator><lastBuildDate>Wed, 24 Jun 2026 21:39:19 GMT</lastBuildDate><atom:link href="https://ai-engineer-in-public.hashnode.dev/rss.xml" rel="self" type="application/rss+xml"/><language><![CDATA[en]]></language><ttl>60</ttl><item><title><![CDATA[🚀 From AI Developer to AI Ops Engineer: Why I'm Going Back to Linux Fundamentals]]></title><description><![CDATA[Introduction:
Everyone is talking about AI Agents, RAG systems, MCP servers, and fine-tuning.
I was too.
As an AI Developer, I spent months building AI applications and experimenting with LLMs. But I ]]></description><link>https://ai-engineer-in-public.hashnode.dev/from-ai-developer-to-ai-ops-engineer-why-i-m-going-back-to-linux-fundamentals</link><guid isPermaLink="true">https://ai-engineer-in-public.hashnode.dev/from-ai-developer-to-ai-ops-engineer-why-i-m-going-back-to-linux-fundamentals</guid><category><![CDATA[Linux]]></category><category><![CDATA[AI]]></category><category><![CDATA[#AIOps]]></category><category><![CDATA[mlops]]></category><category><![CDATA[Devops]]></category><category><![CDATA[Cloud Computing]]></category><category><![CDATA[AWS]]></category><dc:creator><![CDATA[Dinakaran]]></dc:creator><pubDate>Sun, 21 Jun 2026 06:26:57 GMT</pubDate><enclosure url="https://cdn.hashnode.com/uploads/covers/6a3778f564f7308e4010e42e/8b798b32-da6b-46ca-8d69-6af245bdd046.png" length="0" type="image/jpeg"/><content:encoded><![CDATA[<h2>Introduction:</h2>
<p>Everyone is talking about AI Agents, RAG systems, MCP servers, and fine-tuning.</p>
<p>I was too.</p>
<p>As an AI Developer, I spent months building AI applications and experimenting with LLMs. But I eventually realized something:</p>
<blockquote>
<p>Building an AI application is only half the job.</p>
</blockquote>
<p>The real challenge begins when you try to deploy, monitor, scale, and maintain that application in production.</p>
<p>That's where AI Ops, MLOps, Linux, networking, and infrastructure come into the picture.</p>
<p>So instead of chasing every new AI framework, I've decided to strengthen the foundations.</p>
<p>This blog marks the beginning of my journey from AI Developer to AI Ops Engineer.</p>
<hr />
<h2>Why Linux?</h2>
<p>Every AI system eventually runs somewhere.</p>
<p>Whether it's:</p>
<ul>
<li><p>An AI Agent</p>
</li>
<li><p>A RAG application</p>
</li>
<li><p>A Fine-Tuned LLM</p>
</li>
<li><p>A Vector Database</p>
</li>
<li><p>A Kubernetes Cluster</p>
</li>
</ul>
<p>At the end of the day, most production AI systems run on Linux.</p>
<p>Understanding Linux isn't optional.</p>
<p>It's a core skill.</p>
<hr />
<h2>What I've Learned So Far</h2>
<h3>Navigation</h3>
<pre><code class="language-bash">pwd
ls
ls -l
ls -a
cd
</code></pre>
<h3>Files &amp; Folders</h3>
<pre><code class="language-bash">mkdir
touch
cp
mv
rm
rm -r
</code></pre>
<h3>Viewing Files</h3>
<pre><code class="language-bash">cat
less
head
tail
</code></pre>
<h3>Searching</h3>
<pre><code class="language-bash">grep
find
</code></pre>
<h3>Permissions Concepts</h3>
<h4>Files</h4>
<pre><code class="language-text">r = read
w = modify
x = execute
</code></pre>
<h4>Directories</h4>
<pre><code class="language-text">r = list contents
w = create/delete/rename entries
x = enter directory
</code></pre>
<hr />
<h2>What I'm Learning Next</h2>
<ul>
<li><p>chmod</p>
</li>
<li><p>chown</p>
</li>
<li><p>ps</p>
</li>
<li><p>top</p>
</li>
<li><p>kill</p>
</li>
<li><p>ping</p>
</li>
<li><p>netstat</p>
</li>
<li><p>ss</p>
</li>
<li><p>journalctl</p>
</li>
<li><p>Bash scripting</p>
</li>
<li><p>Docker</p>
</li>
<li><p>Kubernetes</p>
</li>
<li><p>CI/CD</p>
</li>
<li><p>Monitoring</p>
</li>
<li><p>Observability</p>
</li>
</ul>
<hr />
<h2>A Realization</h2>
<p>Most AI engineers focus on:</p>
<ul>
<li><p>Prompt Engineering</p>
</li>
<li><p>RAG</p>
</li>
<li><p>Agents</p>
</li>
<li><p>Fine-Tuning</p>
</li>
</ul>
<p>But when production goes down at 2 AM, none of those skills help unless you can understand the infrastructure underneath.</p>
<p>The engineers who stand out are the ones who can:</p>
<ul>
<li><p>Build AI systems</p>
</li>
<li><p>Deploy AI systems</p>
</li>
<li><p>Monitor AI systems</p>
</li>
<li><p>Scale AI systems</p>
</li>
</ul>
<p>That's the direction I'm heading.</p>
<hr />
<h2>What's Next?</h2>
<p>This is Day 1 of my AI Ops journey.</p>
<p>I'll be documenting everything I learn:</p>
<ul>
<li><p>Linux</p>
</li>
<li><p>Networking</p>
</li>
<li><p>Docker</p>
</li>
<li><p>Kubernetes</p>
</li>
<li><p>MLOps</p>
</li>
<li><p>AI Ops</p>
</li>
<li><p>Production AI Systems</p>
</li>
</ul>
<p>If you're on a similar journey, let's connect and learn together.</p>
<hr />
<h3>Final Question</h3>
<p>What Linux command completely changed the way you work?</p>
<p>I'd love to learn from your experience.</p>
<img src="https://cdn.hashnode.com/uploads/covers/6a3778f564f7308e4010e42e/bacc9634-f6cb-424a-b8aa-15305c6303b6.png" alt="" style="display:block;margin:0 auto" />

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<img src="https://cdn.hashnode.com/uploads/covers/6a3778f564f7308e4010e42e/98e0f86c-25b5-4a4d-89c2-0f54d61158ef.png" alt="" style="display:block;margin:0 auto" />

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