Use the categories below to structure your practice. Aim to answer with clear reasoning, trade-offs, and concrete outcomes. Where AI is in scope, emphasize safety, evaluation, and observability.

Coding / Algorithms

Expect to implement solutions in your preferred language with attention to complexity and testability.

• Implement k-most frequent items with streaming input and memory constraints
• Design a scheduler to run tasks with cooldowns; analyze time/space complexity
• Merge and deduplicate large sorted lists efficiently; discuss I/O patterns
• Validate and transform semi-structured documents robustly; add edge-case tests
• Implement a concurrency-safe cache with TTL and LRU eviction

System Design / Architecture

Interviewers will test your ability to scale, secure, and operate services on AWS.

• Design a low-latency search service over legal documents with citations
• Propose a multi-region architecture with controlled failover and data residency
• Introduce canary releases and automated rollback with metrics-based gates
• Choose between SQL/NoSQL for audit-heavy workloads; justify schema and indexing
• Build a telemetry pipeline to power SLOs and track error budgets

AI/ML & LLMs (Role-Dependent)

Demonstrate practical RAG, model integration, evaluation, and governance.

• Architect a RAG system with hybrid retrieval and prompt assembly
• Detect and mitigate hallucinations; define evaluation metrics and thresholds
• Optimize cost/latency for LLM features using caching and prompt strategies
• Compare vector databases for scale and filtering; choose embedding strategies
• Design a human-in-the-loop review process for high-stakes outputs

Behavioral / Leadership

Show ownership, clarity, and values alignment.

• Tell me about a time you challenged (y)our thinking and changed a decision
• Describe a failure, what you learned, and how you acted fast/learned fast
• How did you mentor someone to raise quality and throughput?
• Share a time you balanced customer urgency with technical risk
• How do you drive consensus across Product, UX, and Engineering?

Product, Quality & Ethics

Connect technical choices to customer outcomes, safety, and compliance.

• Define success metrics for an AI drafting feature; how would you monitor drift?
• How would you make AI answers explainable and trustworthy for legal users?
• Describe your approach to privacy and PII handling in training and inference
• Walk through a launch plan with bake-in quality, guardrails, and rollback
• Prioritize a roadmap under constraints; justify sequencing with impact

Deep Dive into Evaluation Areas

Coding & Computer Science Fundamentals

Strong coding is a constant. You will write production-quality code, optimize under constraints, and validate correctness. Interviews emphasize readable structure, appropriate data structures, testability, and performance awareness.

Be ready to go over:

• Data Structures & Algorithms: Arrays, hash maps, stacks/queues, trees/graphs, sorting/searching, heap/priority queues, two-pointer and sliding window patterns.
• Complexity & Optimization: Time/space trade-offs, early pruning, and scaling considerations.
• Testing & Reliability: Unit tests, property-based tests, edge-case analysis, and handling failures gracefully.
• Advanced concepts (less common): Concurrency primitives, lock-free patterns, streaming/iterators, memory models.

Example questions or scenarios:

• "Implement an efficient autocomplete with prefix constraints and frequency ranking."
• "Design a rate limiter with burst handling and fairness guarantees."
• "Refactor this legacy function for readability and testability; add tests for edge conditions."

System Design & Cloud Architecture (AWS)

You will architect services that are secure, observable, and cost-effective. Expect to decompose systems, choose storage and messaging, justify consistency models, and plan for deployment, monitoring, and incident response.

Be ready to go over:

• Service Decomposition & APIs: REST/gRPC design, versioning, pagination, idempotency, and backward compatibility.
• Storage & Caching: SQL/NoSQL choice, indexing, partitioning, TTL/eviction strategies, read/write patterns.
• Observability & Operations: Metrics, logs, traces, SLO/SLA, error budgets, blue/green and canary deploys.
• Advanced concepts (less common): Event-driven architectures, CQRS, multi-region failover, chaos testing.

Example questions or scenarios:

• "Design a document search service with billions of records: indexing, caching, and query latency targets."
• "How would you introduce canary deployments with automated rollback using AWS services?"
• "Plan a migration from a monolith to microservices while minimizing customer impact."

AI/ML & LLM Engineering

For AI-focused roles, depth here is essential. Interviewers assess how you design RAG, manage embeddings/vector databases, integrate LLMs via APIs, and ensure robust evaluation, safety, and monitoring at scale.

Be ready to go over:

• RAG & Retrieval: Indexing strategies, chunking, hybrid search, prompt assembly, citation grounding.
• Model Integration: API orchestration, tool-use/agents, latency and cost controls, caching, fallbacks.
• Evaluation & Safety: Offline evals, human-in-the-loop, hallucination mitigation, content and privacy controls.
• Advanced concepts (less common): Agent workflows, function-calling vs. toolformer patterns, guardrails, model distillation, synthetic data.

Example questions or scenarios:

• "Design a RAG pipeline for legal research with explainability and citation integrity."
• "How would you detect and mitigate hallucinations in an AI drafting assistant?"
• "Compare vector DB choices for low-latency retrieval under heavy concurrent load."