Below are representative questions by area. Use them to audit your readiness and to build story-based answers that demonstrate impact, rigor, and judgment.

Coding and TypeScript

Expect to implement realistic features and utilities with tests and clear error handling.

• Build a NestJS endpoint that supports pagination, filtering, and input validation; include tests.
• Refactor a React component to remove unnecessary re-renders and improve accessibility.
• Implement a typed wrapper that enforces schema-validated responses from a third-party API call.
• Write a streaming data handler that safely parses partial payloads and back-pressure signals.
• Add robust error handling and retries with exponential backoff to a provider integration.

System Design / Architecture

Demonstrate clear contracts, scalability, and observability from day one.

• Design an event-driven pipeline for matching families to providers with SLAs and DLQ handling.
• Propose a migration from a monolith to a service with versioned APIs and rollout strategy.
• Architect a long-running execution service for agents with cancellation, retries, and auditability.
• Evolve data models for personalization while preserving privacy and enabling experimentation.
• Outline a caching strategy that improves P95 latency without sacrificing freshness.

AI/LLM Platform and Safety

Show provider abstraction, evaluation rigor, and safety-first design.

• Design a prompt management system with versioning, approvals, and rollback policies.
• Implement structured output validation and repair for an LLM integration.
• How would you contain provider SDK churn so consuming apps remain stable?
• Define online and offline evaluation metrics and gates for non-deterministic outputs.
• Describe a PII detection/redaction pipeline that guarantees privacy in logs and traces.

Behavioral / Leadership

Demonstrate ownership, clarity, and mission alignment.

• Tell us about a time you aligned multiple teams behind a risky but high-upside change.
• Describe a production incident you drove to resolution—root cause, fix, and prevention.
• Share how you mentored engineers to adopt a new standard or platform abstraction.
• Give an example of turning ambiguous goals into measurable milestones and outcomes.
• How have you balanced speed and safety when shipping AI-enabled features?

Problem-Solving / Case Studies

Expect applied, product-centric problem solving with clear tradeoffs.

• Improve conversion on a search-to-consult flow—what data, experiments, and guardrails?
• You inherit flaky tests and slow builds—what’s your triage and stabilization plan?
• A provider API deprecates a key endpoint—walk through mitigation and rollout.
• Web Vitals regress after a redesign—diagnose, prioritize, and fix.
• An LLM agent hallucinates provider details—design an end-to-end mitigation.

System Design and Architecture

You’ll be evaluated on your ability to design extensible, observable, secure systems that integrate web frontends, APIs, event streams, and data stores. Interviewers look for crisp diagrams, explicit contracts, and reasoning about throughput, latency, and guardrails.

Be ready to go over:

• API design: versioning, pagination, idempotency, contracts (REST/GraphQL), authN/Z
• Event-driven patterns: Kafka/Redis streams, retries/DLQs, idempotency keys, eventual consistency
• Data modeling: Postgres schema design, indexing, migrations, partitioning, caching layers
• Advanced concepts (less common): multi-tenant isolation, backpressure strategies, CDC, real-time comms (WebSocket/WebRTC/Twilio)

Example questions or scenarios:

• “Design a provider-matching service that ingests events, ranks results, and serves a real-time UI.”
• “Propose an architecture for long-running tasks (Agent-as-a-Service) with retries, observability, and cancellation.”
• “Evolve a monolith endpoint into a service with clear SLAs, rate limiting, and rollbacks.”

AI/LLM Platform Fundamentals

Even if you’re not on the Agentic Platform team, you should understand what it takes to build safe, reliable AI features. Interviewers assess how you abstract providers, manage prompts, evaluate non-deterministic outputs, and protect users and data.

Be ready to go over:

• Provider abstraction: adapters for OpenAI/Anthropic/Google, token accounting, streaming
• Safety/guardrails: PII detection/redaction, content filters, schema-constrained outputs, audit logging
• Evaluation: datasets, metrics (exact match, LLM-as-judge), CI/CD gates, regression detection
• Advanced concepts (less common): tool invocation patterns, context packing, prompt versioning, offline vs. online evaluation

Example questions or scenarios:

• “Design a prompt-lifecycle system with versioning, approvals, and rollback integrated with GitHub.”
• “Build a structured-output layer that validates and repairs LLM responses against a TypeScript schema.”
• “How do you contain provider churn so downstream apps don’t break with each SDK update?”

Data, Observability, and Reliability

You will be asked how you know a system is healthy and improving. Expect questions about metrics selection, tracing, log quality, and alerting that reflects user impact. Reliability and performance are responsibilities—own them.

Be ready to go over:

• Metrics/SLIs/SLOs: latency, error budgets, Web Vitals, queue depth, token spend
• Observability: structured logs, distributed tracing, correlation IDs, redaction
• Perf/Resilience: caching strategies, circuit breakers, backoff, bulkheads
• Advanced concepts (less common): privacy-first telemetry, sampling strategies, chaos testing

Example questions or scenarios:

• “Instrument a feature from scratch: what do you log, trace, and alert on—and why?”
• “Reduce a P95 latency regression without increasing error rates—what levers do you pull?”
• “Design a redaction layer that guarantees no PII leaves the boundary of observability tools.”

Product Thinking and Cross-Functional Collaboration

Engineering decisions must be tied to customer value and business outcomes. Interviewers will look for how you size impact, create alignment, and iterate with stakeholders across Product, Marketing/SEO, Data, and Design.

Be ready to go over:

• Prioritization: value vs. effort framing, milestone slicing, risk management
• Experimentation: A/B testing, metrics definition, guardrails, interpretation pitfalls
• Documentation & influence: ADRs, design docs, RFCs, cross-team demos
• Advanced concepts (less common): SEO-aware architecture, content modeling, schema.org usage

Example questions or scenarios:

• “You discover a dependency risk mid-sprint—walk us through stakeholder alignment and path to green.”
• “Propose an experiment to improve conversion on the care-search flow. What metrics and guardrails?”
• “Translate a vague business goal into technical milestones with clear success criteria.”