Walmart Labs AI Engineer Case Interview: Designing a Retail-Scale LLM Assistant and Search Platform

This case simulates how an AI Engineer at Walmart Labs (Walmart Global Tech) would design, launch, and operate an LLM-powered assistant and search experience that serves store associates and customers across Walmart’s omnichannel surfaces (stores, web, app, and voice). The interview mirrors real Walmart Labs interviews: pragmatic, metrics-driven, and cost-conscious, with emphasis on building reliable ML systems at massive scale and partnering with product, platform, and compliance teams. What you’ll be asked to do: - Problem framing: Clarify user segments (customers, store associates, customer care), primary jobs-to-be-done (product discovery, substitutions, inventory lookups, policy Q&A), and success metrics tied to business outcomes (conversion rate, in-stock substitution success, CS average handle time, customer NPS, cost per 1K interactions). - System/ML design: Propose an end-to-end architecture for an LLM assistant and search relevance stack: retrieval-augmented generation over Walmart catalog, inventory, policies, and store data; embedding and index strategy; guardrails/hallucination mitigation; multilingual support; fallback strategies when models or data are unavailable; near-real-time updates for price and availability. - Model choices and cost discipline: Compare hosted LLMs vs. open-source models; token/call budgeting, caching, distillation, prompt compression, response truncation, and selective RAG to control spend while meeting SLAs. Discuss privacy/PII handling (customer data, pharmacy/health contexts), data residency for international markets, and alignment with responsible AI guidelines. - MLOps and reliability: Outline CI/CD for models and prompts, feature/embedding pipelines, online/offline evaluation, canary rollouts, shadow traffic, blue/green deploys, and observability (latency, cost per request, grounding/faithfulness scores, safety flags). Include on-call/runbook considerations for 24/7 retail operations and edge constraints in stores. - Experimentation: Define north-star and guardrail metrics, attribution, A/B and interleaving tests for search and assistant responses, experiment ramp strategy, and how to analyze trade-offs (e.g., conversion lift vs. cost and latency). - Trade-offs at Walmart scale: Discuss resiliency (multi-region, multi-cloud readiness), index sharding and freshness SLAs for price/inventory, latency targets across devices and networks, and how to degrade gracefully during peak events (Black Friday, large promos). Typical session flow (reflects Walmart Labs style): - 0–5 min: Clarify goals, constraints, and stakeholders; align on measurable outcomes. - 5–25 min: Architecture and data design (APIs, retrieval layer, vector store, safety/guardrails, caching, multimodal roadmap). - 25–40 min: Model strategy and MLOps (model selection, prompt/adapter training, embeddings pipeline, deployment, monitoring, incident response). - 40–50 min: Metrics and experimentation (success/guardrail metrics, test design, sampling, statistical power, bias/fairness checks). - 50–60 min: What-if drills (cost-down by 50%, offline mode for stores, expansion to a new country, or moving sensitive flows to a different provider) + brief Q&A. Evaluation rubric used by interviewers: - Customer and business impact (ties design to conversion, pick/pack success, AHT, NPS) – strong signal when the candidate quantifies trade-offs. - Engineering pragmatism and cost stewardship (clear SLAs/SLOs, cost models, caching/distillation strategies, capacity planning). - Scale, reliability, and security (multi-region resilience, data partitioning, PII handling, incident playbooks). - Responsible AI (safety policies, hallucination controls, bias/fairness checks, auditability). - Collaboration and clarity (drives alignment with product, data, platform, and legal/compliance; communicates crisp trade-offs). Representative prompts and follow-ups: - Core prompt: “Design a retrieval-augmented LLM assistant that answers product, order, and policy questions for associates and customers, grounded in real-time price and inventory, with P95 latency ≤ 500 ms for search answers and ≤ 1.5 s for complex policy Q&A, and with strict cost caps.” - Follow-ups: “Cut inference cost by 50% without hurting conversion”; “Handle a Black Friday traffic spike 10× baseline”; “Expand to a new country with data residency rules”; “Mitigate hallucinations on recalls and pharmacy topics.” What good looks like at Walmart Labs: - Starts from customer outcomes, then works backward to a lean but resilient architecture. - Quantifies latency/cost/quality trade-offs; proposes iterative launch and guardrails. - Demonstrates hands-on familiarity with vector indexes, prompt/retrieval tuning, offline/online eval, and experimentation at scale. - Surfaces risks early (privacy, compliance, safety) and proposes concrete mitigations.