PodReadyInterview prep for AI infrastructure roles

For DevOps, platform and SRE engineers

You know Kubernetes. Now prove you can run AI on it.

4 questions · 2 minutes · Based on 17 AI-platform job posts

AI-platform interviews ask about GPU scheduling, model serving and LLM gateways. Practise on broken systems, answer an AI interviewer who keeps asking "why?", and walk in ready in 7 days.

Take the challengeSee the Playbook

Start free. Get the Playbook when you're ready.

$ kubectl get pods -n inferenceNAME                         READY  STATUS             RESTARTS  AGEvllm-llama-8b-6d9f7c-x2kqp   0/1    Pending            0         14mvllm-llama-8b-6d9f7c-9hzr4   0/1    CrashLoopBackOff   6         14mllm-gateway-7b5c9d-ql2mn     1/1    Running            0         2d$
Interviewer

We just rolled out a Llama 8B model on vLLM. Nothing is serving. Two replicas, two different failures. Ready to walk me through it?

Tools named in the job posts

  • vLLM
  • KServe
  • Triton
  • Kueue
  • MIG
  • Langfuse
  • MLflow
  • Argo CD
  • OpenTelemetry

What AI-platform job posts ask for

The fundamentals still matter. The AI layer is what's new.

Almost every post still asks for Kubernetes, Terraform and observability. Most now add model serving and GPU work, and many add LLM gateways, RAG and evaluation. That layer is where candidates with strong DevOps backgrounds get caught out.

Kubernetes16/17
Infrastructure as code (Terraform, Helm)15/17
Observability (Prometheus, Grafana, OpenTelemetry)14/17
CI/CD and GitOps13/17
Model serving (vLLM, KServe, Triton, NIM)12/17
GPU scheduling and GPU nodes10/17
LLM ops (gateways, RAG, evals, guardrails)7/17
ML lifecycle (MLflow, model registry)6/17
Core DevOpsAI layer

Playbook

Look inside the Playbook

Contents

  1. Part 1GPUs on Kubernetes5 questions
  2. Part 2GPU scheduling and capacity5 questions
  3. Part 3Model serving5 questions
  4. Part 4Inference performance and SLOs5 questions
  5. Part 5LLM gateways and API operations5 questions
  6. Part 6RAG and data pipelines5 questions
  7. Part 7Observability and cost for AI workloads5 questions
  8. Part 8Security, governance and delivery5 questions

Walkthroughs

  1. 1The model won't serve
  2. 2Cut streams and a 429 storm at the gateway
  3. 3Token spend doubled overnight

Part 1 · Q3

Why taint GPU nodes, and how do GPU workloads land on them?

What they test

scheduling primitives applied to expensive nodes.

Strong answer

A NoSchedule taint such as nvidia.com/gpu keeps ordinary pods off nodes that cost far more per hour. GPU workloads add a matching toleration, plus a nodeSelector or node affinity on labels like nvidia.com/gpu.product. A toleration only allows a pod onto the node; the selector is what attracts it. Some clusters enable the ExtendedResourceToleration admission plugin, which adds the toleration automatically to any pod that requests a GPU.

Get the Playbook

Start today

Prepare tonight. Practise for real from 15 November 2026.

One-time payments. No subscription.

Playbook

Available now

€29

  • 40 AI-infrastructure interview questions with model answers
  • What each question tests, and the follow-up to expect
  • 3 incident walkthroughs to rehearse out loud
  • 3 labs you run on your laptop, no GPU needed
  • 7-day study plan
  • Delivered instantly as a PDF
Get the Playbook

Sprint · 7 days

For interviews within 30 days

€39

  • Now: the Playbook, instantly
  • From 15 November 2026: gap analysis for your job post
  • 3 mock interviews with follow-ups
  • 6 hands-on labs matched to your offer
  • Evidence-based report after each run
Pre-order Sprint

Intensive · 14 days

Founding price

€69

  • Everything in Sprint
  • Unlimited mock interviews (fair use)
  • Full lab track for your role
  • System design round and progress report
Pre-order Intensive

Secure checkout by Lemon Squeezy · Instant download, link in your receipt

The Playbook is yours the moment you pay. The platform opens on 15 November 2026; if it doesn't, Sprint and Intensive are refunded.

Founding prices for the first 50 Sprint and Intensive pre-orders. Your 7 or 14 days start when you choose, not when you pay.

The platform · from 15 November 2026

One job post in, one prepared candidate out

Everything is built around the specific role you are interviewing for, so you spend your time on the gaps that matter for that offer.

Example

Pasted job post

Senior Platform Engineer, AI Infrastructure

  • Run production Kubernetes with dedicated GPU node pools.
  • Deploy and scale model serving (vLLM, KServe).
  • Operate an LLM gateway: rate limits, retries, cost per tenant.
  • Terraform, Argo CD and OpenTelemetry across environments.

Your skill map for this offer

  • KubernetesStrong
  • Infrastructure as codeStrong
  • ObservabilityPartial
  • LLM gateway opsPartial
  • Model servingGap
  • GPU schedulingGap

Practise first: GPU scheduling, then model serving.

Labs

Break it, read it, fix it, explain it

Short scenarios built from the failures AI platforms actually have. Each one maps to a skill in your job post, and each ends with the interviewer asking you to justify your choices.

Available at launch on 15 November 2026. GPU nodes are simulated, so you practise the real kubectl workflow without paying for GPUs.

kubernetes · gpuPending

The model won't serve

FailedScheduling, taints, tolerations and GPU requests on a shared node pool.

Why doesn't adding another node fix it?

model servingCrashLoopBackOff

Model server crashes on start

A vLLM deployment runs out of GPU memory during startup after a config change.

Which setting do you change, and what does it cost you?

llm gateway429 / 504

Cut streams and a 429 storm at the gateway

Provider rate limits, retries and streaming responses cut by the ingress timeout.

How do you protect the provider quota during an incident?

rag pipelinep95 4.8s

RAG answers got slow

Latency climbs after a re-index. Embedding jobs and the vector database share a node.

Which metric proves your hypothesis before you change anything?

observability · finops+112% tokens

Token spend doubled overnight

A retry loop and missing per-tenant metrics. Build the dashboard that finds it.

What alert would have caught this on day one?

rolloutcanary 10%

Canary of a new model version

Traffic split between two model versions with quality and latency gates.

What are your rollback criteria, and who decides?

Feedback you can act on

Scored on your reasoning, with the evidence attached

Each criterion is checked against what you actually typed and said. The AI explains; deterministic checks decide whether a step was right. No mystery score out of 100.

  • Reading the symptoms
  • Ordering hypotheses
  • Safety of the fix
  • Explaining trade-offs

Lab: The model won't serve

Sample report
CriterionResultWhat we saw
Reading the symptomsStrongOpened the FailedScheduling events before touching the deployment.
Ordering hypothesesPartialChecked node capacity first and missed the taint until the third command.
Safety of the fixStrongAdded a toleration scoped to the GPU pool instead of removing the taint.
Explaining trade-offsNeeds workSaid what you did, rarely why. The interviewer had to ask twice.
Next drillTaints, tolerations and GPU node pools: two short labs, then a 10-minute oral follow-up.

Questions

Before you ask

Still unsure? [email protected]

My interview is before 15 November 2026. What should I buy?

The Playbook. It's delivered instantly and covers the AI-infrastructure questions, with 3 labs you can run tonight. If you have later rounds, the Sprint includes the Playbook and adds mock interviews and labs from 15 November 2026.

What exactly is in the Playbook?

40 questions in 8 parts (GPUs on Kubernetes, GPU scheduling, model serving, inference performance, LLM gateways, RAG, observability and cost, security and delivery). Each has what it tests, a model answer and the follow-up to expect. Plus 3 incident walkthroughs with laptop labs and a 7-day plan.

I've never run GPUs or LLMs in production. Can I still prepare?

Yes, that's the main use case. The labs start from the Kubernetes you know and add the AI layer step by step. You don't need a GPU.

Is this just ChatGPT with an interview prompt?

No. The interview is anchored to your job post and a skill map for the role. Labs run commands against systems we broke on purpose, and scoring uses fixed criteria and automatic checks. The AI runs the conversation and explains the feedback; it doesn't decide alone whether your fix was right.

Do the labs use a real terminal?

Yes. You type real kubectl, curl and journalctl commands in your browser against an isolated practice environment.

Do you promise the real interview questions?

No, nobody honestly can. We train the skills the job post asks for, which is what the real questions test.

What happens to my CV and job post?

Used only to build your plan, deleted on request. Stored in the EU.

Can I get a refund?

The Playbook is a digital product delivered instantly, so its sale is final once delivered, except where consumer law in your country gives you a right to cancel. If the platform doesn't open on 15 November 2026, Sprint and Intensive are refunded.

4 questions. 2 minutes. See where you stand.

Take the challengeGet the Playbook