🧠 AI Architect

Fast-track roadmap: design AI platforms and lead teams delivering AI at scale.

Community / Career Guidance / AI Architect

Who this is for

Senior engineers and ML practitioners ready to move from building models to designing the whole system - and guiding teams and stakeholders. This is a step beyond the AI/ML Engineer track.

Phase 1 · Strong Base

Engineering + ML maturity

  • Be solid in software engineering and ML engineering first.
  • Have shipped at least one ML system to production.
  • Understand data pipelines end to end.
Software designML engineeringData pipelines
Phase 2 · System Design

Architecture thinking

  • Learn scalable system design and trade-off analysis.
  • Design data architecture: storage, streaming, and batch.
  • Choose the right tool for latency, cost, and scale.
System designData architectureTrade-offs
Phase 3 · AI Platforms

Design the ML lifecycle

  • Design feature stores, model registries, and serving layers.
  • Standardise pipelines so teams ship models faster.
  • Plan for LLM and agentic workflows where they fit.
Feature storesModel registryServing
Phase 4 · Trust and Governance

Responsible AI at scale

  • Build in security, privacy, and access control.
  • Address bias, explainability, and compliance.
  • Manage cost and performance across the platform.
Responsible AISecurityCost control
Phase 5 · Leadership

Guide teams and stakeholders

  • Create reference architectures and standards.
  • Align business goals with technical decisions.
  • Mentor engineers and review designs across teams.
Reference architectureStakeholder alignmentMentoring

Learn it with Learn2Pro

Deepen your AI foundations with the Python AI Bootcamp, then explore modern orchestration in the Agentic AI Bootcamp and n8n Workflow AI.

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