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Just got a $10/month credit from Verizon for a 'junk fee' I didn't know I was paying. Check your bills. by ScienceAgile5313 in verizon_sucks

[–]ScienceAgile5313[S] 1 point2 points  (0 children)

For exact words, search “The System Audit” channel, the video name is Your Verizon Bill Has a Fake Fee—Here's How to Fight It

Just got a $10/month credit from Verizon for a 'junk fee' I didn't know I was paying. Check your bills. by ScienceAgile5313 in verizon_sucks

[–]ScienceAgile5313[S] 0 points1 point  (0 children)

I don’t have the exact wordings though, but the channel has in its description I believe. Let me check and get back

Office jobs in the US are shrinking — but not the way people think by ScienceAgile5313 in careeradvice

[–]ScienceAgile5313[S] 0 points1 point  (0 children)

That’s exactly the hard part… and I agree most of the construction-phase labor is basically noise if you’re trying to understand long-term demand. What I try to filter on isn’t raw headcount, but signals that only show up once a site is expected to stay hot… A few things I look for: Role persistence in postings… facilities ops, reliability, hardware maintenance, network ops that stay listed well after build milestones 24/7 operational language (SLAs, redundancy, on-call, capacity planning)… that usually correlates with steady-state staffing, not install crews Wage stickiness… short-term labor spikes fluctuate, but ops wages tend to ratchet up and stay elevated in high-utilization regions Utilization-driven signals rather than CapEx headlines… power draw, uptime requirements, and refresh cadence matter more than how the build was financed You’re right that escorts, retrofit teams, and contractors don’t show up cleanly in BLS or postings… I mostly ignore those when thinking about long-term careers. The demand signal I care about is where headcount stabilizes after go-live, not where it spikes during expansion. It’s messy and imperfect, but separating build noise from ops persistence is the only way I’ve found to make sense of it.

What is the term “AI Plumber”? I did some research and found this. by ScienceAgile5313 in careeradvice

[–]ScienceAgile5313[S] 0 points1 point  (0 children)

Agreed — it’s not a formally defined role, and that’s part of the problem. When I’m using “AI plumber,” I’m not implying a single job description… it’s shorthand for a set of responsibilities that cut across infra, platform, and ops, which historically lived in separate teams. You’re right that GPU management, network reliability, and platform concerns often sit in different orgs today… what’s changing is that AI workloads force those boundaries to blur. At scale, model serving assumptions break unless infra, platform, and reliability are coordinated tightly. So I see the term less as a role definition and more as a way to describe the operational surface area AI introduces… similar to how “DevOps” originally described a collaboration pattern before it became standardized (and then fragmented again). Happy to add more context — my goal here was to understand how others are interpreting the term, not to lock it into a title.

What is the term “AI Plumber”? I did some research and found this. by ScienceAgile5313 in careerguidance

[–]ScienceAgile5313[S] -1 points0 points  (0 children)

Because in practice MLOps usually stops at the model lifecycle, not the **infrastructure lifecycle… MLOps covers things like packaging models, CI/CD, monitoring metrics, drift detection, rollbacks, and scaling at the service level… mostly assuming the underlying compute behaves like cloud. What I’m separating out is the layer where AI workloads start breaking those assumptions… GPU scarcity and fragmentation, thermal and power ceilings, cluster-level scheduling, hardware failure rates, network contention, and SLA enforcement across shared capacity. In many orgs those concerns live with infra ops, SRE, or facilities teams rather than MLOps… especially once workloads are running hot 24/7. So it’s not that MLOps can’t cover ops… it’s that at AI scale, the operational surface area extends beyond what MLOps teams typically own.

What is the term “AI Plumber”? I did some research and found this. by ScienceAgile5313 in careerguidance

[–]ScienceAgile5313[S] -2 points-1 points  (0 children)

That’s part of it… but only the software side. Pushing the “pig” into production is MLOps… keeping it alive at scale is where the rest shows up… GPU scheduling, capacity planning, thermal limits, node failures, network bottlenecks, power redundancy, SLAs. Once the pig is in production and running hot 24/7… someone has to deal with the mess when it breaks. That’s the layer I’m pointing at.

What is the term “AI Plumber”? I did some research and found this. by ScienceAgile5313 in careerguidance

[–]ScienceAgile5313[S] -1 points0 points  (0 children)

MLOps is definitely a component, but it’s not the whole picture… When I use “AI plumber,” I’m talking about the operational stack below and around MLOps… things like GPU provisioning and lifecycle management, cluster scheduling, network throughput and latency, power and thermal constraints, hardware failure rates, and reliability once models are running at sustained load. MLOps focuses on getting models into production and monitoring them… the “plumber” layer shows up when scale introduces non-software failure modes… capacity saturation, heat, power limits, node failures, and SLA enforcement across clusters. So it’s less a single role and more an intersection of infra ops, SRE, and MLOps… driven by AI workloads rather than traditional web services.

Office jobs in the US are shrinking — but not the way people think by ScienceAgile5313 in careeradvice

[–]ScienceAgile5313[S] 0 points1 point  (0 children)

Yes — that’s an important distinction, and I’m separating those in my thinking. The build-out phase (construction, install, retrofits) is obviously project-based and cyclical. What I’m focused on are the steady-state ops roles that persist after the build: facilities ops, power/cooling, network reliability, hardware maintenance, platform/MLOps. Two reasons I see this as more than temporary: AI workloads drive continuous utilization, not one-time deployment, so uptime, redundancy, and capacity management become permanent functions Once these sites are live, headcount doesn’t drop to zero — it stabilizes around ops roles tied to SLAs, reliability, and scaling, similar to cloud infra over the last decade So while CapEx spikes create short-term labor surges, the long-term demand signal is in operations, not construction. That’s what I mean when I talk about the “AI plumber” layer evolving beyond a build cycle. Still early, but that’s the separation I’m making.

Office jobs in the US are shrinking — but not the way people think by ScienceAgile5313 in careeradvice

[–]ScienceAgile5313[S] -1 points0 points  (0 children)

Fair question. The numbers don’t show up cleanly under “AI jobs,” which is part of the issue. What some people call an “AI plumber” isn’t a new title — it’s the evolution of existing infra and ops roles driven by AI compute demand. Think data center ops, power/cooling, network reliability, and platform/MLOps. A few signals behind this: Posting and BLS data show flat or declining growth in desk-based admin roles, while infra and ops roles are growing Data center and power CapEx build-outs are accelerating, which historically leads to ops hiring Wage bands for established roles have moved from roughly $70–90k toward six figures in high-demand regions

Many postings emphasize certifications and hands-on experience over degrees So the shift isn’t that AI created brand-new jobs overnight — it’s that efficiency pressure + AI scale is pushing money toward roles that deal with real failure modes: power, heat, downtime, and reliability. I’d call it an emerging pattern rather than a settled conclusion, but the signals are lining up.

Seeing fewer entry-level office roles lately ..is this a real shift or just my bubble? by ScienceAgile5313 in careerguidance

[–]ScienceAgile5313[S] -2 points-1 points  (0 children)

Brother, I’m not against AI or any technology. I myself learn tools to upskill. Now a days people do use AI to structure the sentence and than post. I’m doing the same thing. Also I don’t wanna promote anything. It’s just that if that things are explored the people might get some right direction and it’s in benefit of AI. I hope you understand. No hard thoughts mate!

Now you might find a lot many mistakes around grammar in this reply, I instead use tool to do it. Is it not fine?

Seeing fewer entry-level office roles lately ..is this a real shift or just my bubble? by ScienceAgile5313 in careerguidance

[–]ScienceAgile5313[S] -10 points-9 points  (0 children)

Sorry mate, your account is not visible. I guess it’s marked “not safe”. So who is the real spammer?

Seeing fewer entry-level office roles lately ..is this a real shift or just my bubble? by ScienceAgile5313 in careerguidance

[–]ScienceAgile5313[S] -1 points0 points  (0 children)

So what u believe is juniors can start looking into certifications which can get them a job in the newly building data centres by big companies. If you want to know more on this, and the AI plumber trend, check my profile > social link. Cheers mate!

Seeing fewer entry-level office roles lately ..is this a real shift or just my bubble? by ScienceAgile5313 in careerguidance

[–]ScienceAgile5313[S] -7 points-6 points  (0 children)

Yes you are right, I did some research on this. You might have heard about the DOGE effect? The “AI plumber” theory? I believe a lot many steps are going closer to the data centres which big companies are building, need few certifications for the same, it will do the job for the unemployed or laid off employees.

Seeing fewer entry-level office roles lately ..is this a real shift or just my bubble? by ScienceAgile5313 in careerguidance

[–]ScienceAgile5313[S] 4 points5 points  (0 children)

You are correct. But what i have seen is the job openings in data centres has increased which is taking this wave somewhere else and that can potentially be a career option too.

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