A structured roadmap to become an AI/ML Architect — curated tutorials, skills checklist, and interview preparation across 6 tracks.
An AI/ML Architect designs the technical vision for machine learning systems at scale. They bridge the gap between data science experimentation and production-grade engineering, making decisions about model serving, data pipelines, infrastructure, and team practices that shape how an organization builds and deploys AI.
Architect end-to-end ML pipelines, model serving infrastructure, and data processing systems.
Evaluate and choose frameworks, cloud services, and tools for ML workloads.
Define best practices, review architectures, and mentor engineering teams.
Translate business requirements into scalable ML solutions.
Senior engineers looking to specialize in AI/ML architecture, ML engineers moving into architecture roles, and data scientists who want to build production systems. You should be comfortable with at least one programming language and have some exposure to machine learning concepts.
Core competencies you need to develop on the path to AI/ML Architect.
Model training, evaluation metrics, feature engineering, hyperparameter tuning.
IntermediateScalability, distributed systems, API design, data modeling.
AdvancedETL/ELT pipelines, data lakes, streaming, data quality.
IntermediateModel serving, feature stores, experiment tracking, MLOps.
AdvancedKubernetes, IaC (Pulumi/Terraform), CI/CD, monitoring.
IntermediateAgent architectures, orchestration, RAG, evaluation, guardrails.
IntermediateFollow this four-phase roadmap to build the skills progressively. Each phase links to curated LIZIU tutorials.
Common questions you should be ready to answer in AI/ML Architect interviews.
Focus on model serving patterns, caching embeddings, horizontal scaling, and latency budgets.
Cover offline/online stores, feature freshness, point-in-time correctness, and data consistency.
Discuss latency, cost, freshness, model complexity, and when to use each approach.
Cover model registry, A/B testing, canary deployments, and monitoring for drift.
Address data validation, schema evolution, incremental processing, and data lineage.
Explain bronze/silver/gold layers, quality gates, and feature engineering at each stage.
Consider team expertise, maintenance burden, vendor lock-in, total cost of ownership.
Cover model metrics, data quality monitoring, alerting thresholds, and incident response.
Continue your learning with these tracks and recommended reading.