What I Would Want to Know When Interviewing an AI Engineer

Hiring an AI Engineer?
Sure, flashy RAG flows and multi-agent demos look cool—but the real challenge is building a reliable, cost-effective system that works in production. Here’s what I would actually want to know during interviews.

End-to-End System Design

Question: Can you design data ingestion → preprocessing → model inference → sserving?

• What I’m looking for:
  • Data pipelines (ETL tools, streaming vs batch)
  • Model hosting (serverless vs containerized)
  • API layers (REST/gRPC, WebSockets)
  • Bottlenecks (I/O, network, compute) and mitigation (caching, sharding)

Cost Estimation & Optimization

Question: How would you estimate hosting, inference, and storage costs? How can you reduce them?

• Details:
  • Pricing models (per-token, per-hour GPU, storage IOPS)
  • Trade-offs: smaller models, mixed precision, spot instances
  • Auto-scaling strategies and cost alerts

Latency vs. Quality Trade-offs

Question: How would you reduce latency? What’s an acceptable latency vs. quality compromise?

• Techniques:
  • Quantization, distillation, pruning
  • Caching frequent responses
  • Async pre-warming of models
  • SLAs: 100ms vs 500ms vs 1s thresholds

Self-Hosted vs. API LLMs

Question: Do you really need self-hosted LLMs? When is it justified?

• Considerations:
  • Data privacy/regulatory requirements
  • Cost at scale vs. API convenience
  • Custom fine-tuning needs
  • Maintenance overhead (updates, scaling)

Fine-Tuning on User Behavior

Question: How would you collect user data, fine-tune models, and serve them?

• Stack:
  • Data capture (logs, feedback widgets)
  • Frameworks (Hugging Face Trainer, LoRA, PEFT)
  • Serving (SageMaker, KFServing, custom FastAPI endpoints)

Dataset Construction & MLOps

Question: How would you design the training dataset, loss function, and MLOps pipeline?

• Key points:
  • Labeling strategy (manual, weak supervision)
  • Loss choices (cross-entropy, contrastive loss)
  • CI/CD for models (GitHub Actions + DVC + Kubernetes)

Database Selection

Question: Which database(s) would you choose for embeddings, metadata, and user data—and why?

• Options:
  • Vector DB (e.g., Pinecone, Qdrant) for similarity search
  • SQL (PostgreSQL) for transactional data
  • NoSQL (MongoDB, Redis) for fast key-value or session stores
  • Hybrid architectures and consistency considerations

Metrics & Monitoring

Question: What metrics would you track, and how?

• Examples:
  • Model performance: accuracy, perplexity, latency, throughput
  • Business metrics: conversion rate, user engagement
  • Tooling: Prometheus + Grafana, MLflow, Weights & Biases

System Debugging & Observability

Question: How would you monitor failures and debug them?

• Tactics:
  • Centralized logging (Elastic Stack, Splunk)
  • Distributed tracing (OpenTelemetry)
  • Alerting on error rates, timeouts, resource exhaustion

Feedback Loops & Continuous Improvement

Question: How would you collect, track, and evaluate user feedback?

• Approach:
  • Online A/B testing frameworks
  • User rating widgets and sentiment analysis
  • Automated retraining triggers based on drift detection

Determinism & Reproducibility

Question: How would you make the system more deterministic?

• Strategies:
  • Seed control in tokenizers and sampling
  • Version-pinning models and dependencies (Conda, Poetry)
  • Immutable artifacts (Docker images, model hashes)

Embedding Updates Without Downtime

Question: How would you swap embedding models and backfill vectors seamlessly?

• Pattern:
  • Blue/green deployment of new embeddings
  • Incremental reindexing in vector DBs
  • Feature-flag gating for gradual rollout

Fallback & Resilience

Question: What fallback mechanisms would you implement?

• Ideas:
  • Rule-based or keyword search backup
  • Cached answers for common queries
  • Circuit breakers to degrade gracefully under load

The “Bonus” Fundamental Questions

• Without LLMs/Vector DBs: How would you solve the problem using classical IR, rules, or heuristics?
• Deep Dive: Explain tokenization and embeddings from first principles.
• Fine-Tuning Mechanics: What happens during training—optimizers, learning rates, layer freezing?

Why these matter:

Too many engineers build complex demos that never ship. I want candidates who understand the fundamentals, can design resilient systems, and can adapt when hype tools don’t fit.

Ready to build production-ready AI? Share your thoughts below!