Throwaway for obvious reasons.

SWE/SRE background, ~7 years experience, applying across engineering and adjacent roles. Laid off earlier this year in a company-wide cut.

I tracked everything since the layoff, so here is the actual funnel:

- 240+ applications
- ~50 companies replied with anything human at all (screen or better)
- 33 reached a first round
- 9 reached a second round
- 4 reached a final round
- 1 offer, which stalled in negotiation
- Everything else: rejected at resume screen, req closed, or ghosted

Mostly just wondering if others are in the same boat. The numbers made me question myself a lot until I laid them out like this and realised most of it never even reached a person. Curious how everyone else's search has been going this year, and how you are holding up.

I'm an SRE at a shop shipping a lot of LLM features and our incident response is basically broken for anything non-deterministic.

Our standard flow is built for the usual suspects like latency, errors, and uptime. If the 5xx rate is low and P99 is fine, our dashboard stays green. But LLMs are giving us soft failures constantly where the API returns a 200 OK and the latency is perfect, but the model is just outputting complete garbage.

Nobody gets paged, but the CX team is losing their minds because users are getting hallucinations. I'm struggling with the severity logic here. Is a 5% drop in eval scores a P3? Does that warrant a 3 AM wake-up call, or do we just treat it as drift for the morning?

I'm trying to wire online eval scores into our alerting pipeline so quality is a first-class metric and not just something we manually hunt down in a trace. We're using Braintrust for the traces, but the bridge between a bad trace and an actionable alert is basically non-existent right now. I need to know how to turn a production hallucination into a high-confidence signal without the noise.

Has anyone actually solved this quality vs. availability mess in prod? How are you deciding what level of dumbness actually deserves a page? I don't want to overengineer the alerts, but I'm tired of CX being the only ones who know the site is broken.

In my last role, we ended up multi cloud mostly by accident, major workloads on AWS, a big legacy system on Azure, some experiments on GCP, and a handful of SaaS platforms that each came with their own identity and billing surface, another provider in every way that mattered. Nobody planned a unified strategy, it just evolved that way. I have since talked to teams who went multi cloud on purpose, picking providers deliberately for specific managed services, so this is not only an accidental sprawl story, though many of what we learned came from cleaning up the accidental version.

We had to figure out which bits were in the right place for real reasons, like latency or compliance requirements and which were just historical accidents. We tried to get identity and backups into something resembling a common pattern, while accepting that each provider has its own quirks. Terraform helped once we agreed on conventions.

The other key piece was visibility: tooling that showed us what was under IaC across every provider we ran and what wasn't, plus a read on cost and risk for each. Without that, multi cloud felt like we were flying blind.

For anyone who is intentionally or unintentionally multi cloud: have you found a way to make it feel like a strategy instead of entropy or is it one control plane stacked on another?

Hi everyone,

I’ve been observing the AI boom across different sectors, and it feels like SRE isn't getting the same level of hype or rapid integration as Software Engineering (SWE) or Cybersecurity. While AI tools for SRE definitely exist, their progress seems slower, and their impact on the job market feels less disruptive so far.

As a Junior , I’m trying to wrap my head around this. It got me wondering:

1. Is SRE inherently more "AI-proof"? Does the high stakes of infrastructure and the "human-in-the-loop" necessity for critical incidents make it harder for AI to take over?

2. The "Invisible" AI: We see AI tools in the space, but they don't seem to have a clear impact on hiring or daily workflows yet. Am I missing something, or are we genuinely in a more "secure" niche compared to pure coding roles?

I’d love to hear your perspectives—especially from those who have been in the industry for a while. Is our field special, or am I just being overly optimistic?

We're getting pushed to use AI for more ops work. At the same time I keep seeing posts here about an agent wiping a prod db or deleting the backups (the Railway/Cursor one especially). Feels backwards that we won't give a mid-level engineer write access to prod, but we'll happily point an agent at it.

How people actually handle the thing that touches prod, human or agent:

• When something needs to change prod (run a runbook, restart a service, rotate a cred, drain a node), how do you control who or what is allowed to do it? Jenkins jobs, break-glass/PIM, Teleport ...?
• Has anyone let an AI agent actually run things in prod, not just read? If so, are you relying on the tool's own guardrails or something you set up yourself?
• The bit I keep getting stuck on: keeping RBAC and the audit trail the same across every tool that can touch prod. Have you got it sorted?

How's everyone handling this?

I have got an offer to join Nvidia on their SRE team which revolves around maintaining or keeping up with the GPU Infra in their compute Infrastructure, and another offer from Palo Alto Networks as a swe, so I just wanted to know like which one to choose, I'm a new grad btw, please let me know your perspective guys