architecture

Arguing against agents is the move that reads senior in AI-lab interviews. The error-compounding math, the decision rule, and the workflow-vs-agent spectrum that wins this contrarian question.

The experience probe that opens half of all applied-AI loops, and the place resumes go to die. The four-beat walkthrough structure interviewers can actually score, plus the two omissions that quietly downgrade you from architect to API caller.

Million-token windows didn't kill RAG; they changed when you reach for it. The decision rule that holds up in production, and the silent failure each option hides behind a confident answer.

Sending every request to the frontier model is the lazy bill. The patterns that route by difficulty, the verifier that enforces a hard quality floor, and the failure mode that turns a cost-saving cascade into a worse, slower system.

Three transformer families, one distinction that decides everything: which tokens are allowed to see which. The attention mask is the whole answer, and it explains why GPT generates and BERT does not.

The warm-up that filters out tutorial-level candidates. Interviewers aren't grading the diagram, they're grading whether you know the three places real pipelines fail and how you'd see it happening.

Arguing against agents reads senior; it's an explicit rubric signal at AI labs. Learn the cost ladder from single call to autonomous loop, and the three conditions that actually justify an agent.

The POC-to-production gap is the FDE's home turf. What actually breaks at each order of magnitude (index, ingestion, latency, cost) and the dependency math that makes 99.9% harder than it sounds.

Multi-agent is the most over-prescribed architecture in the industry. A staff-level answer is a decision framework driven by task structure and context limits, with the orchestration failure modes named before the interviewer asks.

Asked constantly in GenAI loops, and most answers are marketing-page recitals. The version that scores: classify by what each actually is, data framework, integration toolkit, graph runtime, then give the selection criteria that outlive all three names.

Everyone computes the stale fraction as a share of the corpus, gets 0.03%, and moves on. Weighted by queries rather than documents it is 1.4%, roughly fifty times larger, because the documents that changed during the rebuild are the ones people are asking about.

The 2026 FDE differentiator: customers ask for LLMs by name, and the engineer who knows when a $0 logistic regression beats a $40k/month LLM pipeline, with the cost math, wins the room.

Half the value of this question is the second clause. Feature stores solve three specific problems, and FDEs earn trust by naming the team sizes and model counts below which they're expensive overkill.

The capstone panel question that ties the whole track together. The spine, data, training, deployment, monitoring, operating model, plus the maturity-staging judgment that separates architects from slide-readers.

A budget-constrained design problem, not an ideology question. The per-call math that rules an LLM in or out, the cascade that keeps quality while cutting cost 90%, and the SLO arithmetic that decides where the LLM is even allowed to sit.

The architecture question both Databricks and Snowflake ask, for opposite reasons. A vendor-neutral framework, what the Delta transaction log really does, and the honest convergence story that scores with both panels.

A requirements-interrogation question disguised as an architecture question. The first move isn't Kafka, it's a freshness-SLA conversation that usually deletes 90% of the cost. Here's the script strong FDEs run.

The 45-minute MLOps capstone reported from Capital One and Uber loops. The component walkthrough is table stakes, what's graded is sequencing, the batch-vs-realtime call made early, and knowing which pieces you'd cut for v1.

The 'why' is the question: AWS gives you five ways to host a model, and interviewers want the decision tree plus the trade-off you knowingly accepted. The walkthrough structure, the real cost math between SageMaker endpoints and EKS, and the answer shape that survives drilling.

An architecture question with a defensible right answer, and interviewers are tired of 'it depends.' Here's the split that survives both the platform team's consistency argument and the app team's context argument.