System Design & Architecture

• How would you design a scalable ML system for real-time inference?
  • A scalable real-time ML system needs to optimize latency, throughput, and fault tolerance. My approach includes:
  • Data Ingestion: Use a high-throughput event-driven pipeline (Kafka, Pulsar).
  • Feature Store: Serve precomputed features with low-latency lookup (Feast, Redis).
  • Model Serving: Deploy models using TensorFlow Serving, TorchServe, or Triton. Use ONNX for cross-framework compatibility.
  • Scalability: Use horizontal scaling with Kubernetes and auto-scaling based on request load.
  • Caching & Optimization: Implement model quantization (TFLite, FP16/INT8) and caching (Redis, Memcached) for frequent queries.
  • Monitoring: Track inference latency, prediction distribution, and model drift with Prometheus + Grafana.
• Can you describe the architecture of a past ML solution you built and how it handled data, model training, and deployment?
  • At company A, I designed an AI-powered anomaly detection system for cloud infrastructure monitoring.
  • Pipeline:
  • Data streamed from logs via Kafka → Processed in Spark for batch & real-time feature extraction.
  • Model trained with AutoML (HPO using Optuna) and deployed via TorchServe.
  • Real-time inference in a Flask/FastAPI service with a Redis cache to reduce latency.
  • Anomaly alerts sent to monitoring dashboards (Prometheus, Grafana) and auto-healing actions triggered via webhooks.
  • Scalability:
  • Used Kubernetes + Istio for load balancing & traffic control.
  • Implemented multi-armed bandits to select the best anomaly detection model dynamically.
• What trade-offs do you consider when choosing between batch and real-time processing for ML pipelines?
  • Latency vs. Accuracy: Real-time processing prioritizes low latency, but batch jobs can use more complex models.
  • Cost vs. Freshness: Real-time systems require high compute + storage costs (e.g., AWS Lambda vs. EMR batch jobs).
  • Complexity: Streaming pipelines (Flink, Kafka Streams) are harder to manage than batch ETL jobs.
  • Example: In fraud detection, we use a hybrid approach—fast heuristic-based real-time scoring + deeper batch model retraining.

MLOps & Model Deployment

• How do you ensure the reliability and scalability of ML models in production?
  • CI/CD for ML: Use MLflow to track versions, retrain triggers, and deploy models.
  • Model versioning: Serve multiple model versions (A/B testing, canary deployments).
  • Infrastructure: Deploy via Kubernetes with horizontal auto-scaling.
  • Fault tolerance: Use fallback models, graceful degradation mechanisms.
  • Monitoring: Log model predictions, monitor concept drift (EvidentlyAI), and alert on performance degradation.
• What tools and best practices do you recommend for CI/CD in ML workflows?
  • Code Versioning: Git + DVC (Data Version Control).
  • Pipeline Automation: Use Kubeflow Pipelines or Metaflow.
  • Continuous Training: Auto-retrain on new data with feature drift detection.
  • Testing: Unit test ML code (pytest), test model accuracy with holdout datasets before deployment.
  • Deployment: Use Terraform for IaC, Docker, and K8s for scalable model deployment.
• How do you monitor ML models post-deployment to detect drift and ensure performance?
  • Concept & Data Drift: Compare input distribution over time (EvidentlyAI, WhyLabs).
  • Performance Tracking: Log inference latency, accuracy decay, prediction skew.
  • Retraining Triggers: Automate retraining based on drift detection.
  • Alerting: Set up alerts on Prometheus + Grafana for unusual prediction patterns.
  • Versioning: Use time-travel queries to backtest models on past feature values.
• What factors determine your choice of serverless vs server-based deployment?

Data Engineering & Feature Engineering

• How do you design a feature store for a company that runs multiple ML models?
  • Online vs. Offline Features: Use Feast—batch store in BigQuery, online store in Redis.
  • Consistency: Ensure feature parity across training and serving.
  • Caching: Frequently used features should be cached to reduce compute costs.
  • Versioning: Use time-travel queries to backtest models on past feature values.
• What strategies do you use to handle data quality issues in ML pipelines?
  • Data Validation: Use Great Expectations for schema validation.
  • Missing Values: Use imputation (KNN, MICE) or synthetic data generation (SMOTE).
  • Anomaly Detection: Auto-flag outliers using isolation forests.
  • Data Drift Detection: Track data distribution shifts with Kolmogorov-Smirnov tests.
• How do you optimize feature selection and engineering for large-scale datasets?
  • Automated Feature Selection: Use SHAP, LASSO, and PCA.
  • Dimensionality Reduction: Principal Component Analysis (PCA), Autoencoders.
  • Feature Importance: Use XGBoost’s feature importance to prune irrelevant features.
  • Sparse Feature Encoding: For categorical data, use hashing tricks or embeddings.

AI Strategy & Business Impact

• How do you align AI/ML initiatives with business objectives?
  • Identify key pain points and define success metrics (ROI, latency, accuracy).
  • Collaborate with stakeholders to prioritize high-impact models.
  • Ensure AI solutions are explainable, reducing business risk.
  • Example: At company A, I designed an ad ranking model that increased ad CTR by 12%, directly increasing revenue.
• Can you give an example of an AI/ML solution you designed that had a significant business impact?
  • Built personalized product recommendations for an e-commerce client.
  • Used hybrid collaborative filtering (ALS + Transformers).
  • A/B tested, saw 15% increase in conversion rates.
• How do you balance innovation with the practicality of deploying AI solutions at scale?
  • Use cutting-edge techniques, but prioritize simplicity + maintainability.
  • Example: Replaced complex deep learning models with an XGBoost model that performed 98% as well but was 5x faster in inference.

Leadership & Collaboration

• How do you work with software engineers, data engineers, and product teams to deliver ML solutions?
  • Hold cross-functional design reviews.
  • Define clear API contracts between data pipelines and ML models.
  • Translate ML research into production-ready code.
• Have you led a team of ML engineers or data scientists? What’s your leadership style?
  • Yes, I lead with mentorship, ownership, and technical depth.
  • Encourage knowledge sharing and create a culture of experimentation.
• How do you handle technical disagreements when designing ML solutions?
  • Data-driven approach: Use A/B tests to validate decisions.
  • Encourage open discussion: Debate pros/cons objectively.
  • Prioritize business impact: Choose the simplest, most scalable solution.