Serious AI engineering interviews do not stop at prompts. They test code, system boundaries, model behavior, data, security, incident judgment, and whether you can turn ambiguity into a production result.
Practice case · not an employer screen · no placement or affiliation implied
Recover the enterprise copilot pilot
Diagnose the failure, redesign the full-stack boundary, define the eval gate, and recover the customer rollout.
A target is not a job title pasted onto a course. It defines the capability floors, dependency-closed curriculum gate, proof a reviewer should inspect, and the experience we do not yet claim to teach.
Builds and operates user-facing AI features across product, services, agent tooling, evaluation, and deployment boundaries.
AI judgment
Instruction design
System decomposition
Agent systems
Evals & verification
Security & governance
A deployed AI product slice
Operating a high-traffic production service: incident rotation, latency budgets, and distributed-systems depth remain workplace experience.
Turns a business workflow into a measurable AI system, then owns the evaluation, safety boundaries, and iteration needed to make it useful.
AI judgment
Instruction design
System decomposition
Agent systems
Evals & verification
Security & governance
A production-shaped agent workflow
Customer-domain discovery and the political work of winning production access are not simulated by the current curriculum.
Works with a customer under real constraints to diagnose a workflow, deliver a credible technical wedge, and leave behind an operable system.
AI judgment
Instruction design
System decomposition
Agent systems
Evals & verification
Security & governance
A customer-style discovery and delivery brief
Live customer discovery, procurement navigation, and executive-room facilitation require supervised field practice beyond the current modules.
The same seven-station protocol is published for every target. The scenario changes; the dimensions and evidence contract do not. This is our practice standard, not a claim about any employer's private process.
State the target role, its six floors, and the artifact being used as evidence.
A bounded problem statement and a defensible choice of what not to build.
Trace the candidate's own implementation from requirement through deployed behavior.
Inspectable code, configuration, and an explanation that distinguishes authored decisions from generated output.
Design a production-shaped AI workflow under a role-relevant constraint.
A sequenced architecture that has an explicit operator path rather than a diagram-only answer.
Make model instructions, structured outputs, and tool use inspectable and constrained.
Concrete contracts and failure behavior, not framework vocabulary.
Diagnose a failed behavior and decide whether the system may ship.
A task-specific evaluation plan with a release or rollback decision.
Handle data, permissions, monitoring, and escalation as part of technical delivery.
Named control owners and a credible path from detection to human intervention.
Defend tradeoffs, receive per-dimension feedback, and name the next proof-bearing gap.
A calibrated explanation of what is known, what is assumed, and what proof is still required.
The new advanced case is a fictional, clean-room production packet—not trivia and not confidential interview content. The source material is open; the evaluator facts stay held out.
Fictional enterprise customer Northstar Freight has paused a 120-user operations-copilot pilot after unreliable answers, a cross-tenant retrieval incident, and a Monday executive review. You are embedded with the delivery team and must turn the evidence into a safe recovery and production rollout plan.
The plan identifies the client-controlled workspace boundary, stale retrieval, unsupported answers, and retry behavior from the packet while preserving evidence and avoiding unsupported exposure claims.
The revised API, authorization, retrieval, citation, and deletion paths enforce server-side tenant membership and least privilege rather than trusting browser-supplied identifiers.
The plan defines segmented evals, traceable telemetry, reliability controls, explicit thresholds, owners, kill switch, and rollback conditions before re-enable or expansion.
The two-week sequence respects the 15-per-division pilot constraint and gives supervisors, customer success, and operations a concrete feedback and handoff rhythm.
AI scoring is labeled as AI scoring. A live provider can review the submission against held-out facts; an offline heuristic can save work but can never issue a pass.
This case uses four scenario-specific rubric dimensions. Role standing remains the separate, transparent six-dimension profile shown above; neither is an employer-readiness prediction.
The bar can be extremely high without misrepresenting what the program is. We publish the work, the evaluator, and the operating boundary separately.
Labeled technical practice
Three authored role targets, a seven-station practice protocol, one interview case, four curriculum-linked field exercises, durable artifacts, and portfolio evidence are available. A configured model may grade the case; offline review can save work but cannot issue a pass.
Calibrated practitioner panels
Human mock interviews activate one role at a time only after current practitioners are vetted, auditioned, contracted, calibrated, and attached to real capacity.
Degree or employer outcome
This is not a PhD, academic credit, an OpenAI or Anthropic interview, a referral, placement service, or employment guarantee. The evidence is the product.
Role targets cite first-party public sources and carry an observation date. They model recurring work signals; they do not claim access to private interview questions or a relationship with any employer.
OpenAI — Full Stack Software Engineer, Agent Enablement ↗
REVIEWED 2026-08-01SOURCE 02Anthropic — Staff Software Engineer, Claude.ai ↗
REVIEWED 2026-08-01SOURCE 03OpenAI — Product Engineer, Enterprise AI Platform ↗
REVIEWED 2026-08-01SOURCE 04OpenAI — AI Deployment Engineer, Agent Enablement ↗
REVIEWED 2026-08-01SOURCE 05OpenAI — Forward Deployed Engineer, Seattle ↗
REVIEWED 2026-08-01No source was used to infer a universal years-of-experience threshold or a current opening count.
The scan did not establish which framework, cloud provider, or degree requirement is universal across applied AI engineering roles.
The scan does not claim every forward-deployed role carries the same travel expectation, clearance requirement, or industry specialization.
