Everyone is building AI. Almost nobody can architect it.

How to become an AI Architect

A 6-week program in AI architecture and system design. Learn the 7-layer blueprint, draw 4 reference architectures on both AWS and Azure, and walk out with what an architect is actually judged on: diagrams, decision records, SLOs, threat models and runbooks.

45,000+ professionals trained  ·  6 weeks, one module a week  ·  1 year of access

The seven layers you will learn to design

  • Experience
  • Gateway
  • Orchestration
  • Model
  • Data
  • Integration
  • Platform
Every layer taught on AWS and on Azure
6weeks
74lessons
8modules
4reference architectures
7layer blueprint

Our coaches and alumni work at these companies

JPMorganChase
Capgemini
PwC
BNY Mellon
Limited seats · Live cohort

Next Batch Starts In

21 November 2026 | 7:00 AM - 10:00 AM PST
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This batch is live. Enroll now to join the next cohort. Reserve your seat

Who this is for

Architecture roles skew senior: the median experience asked for in AI architecture postings is around seven years. This is for people already working in technology who want the title, not for complete beginners.

  • Cloud & solution architectsYou can architect systems. Now do it for AI, with the vocabulary and the trade-offs
  • Senior engineers & tech leadsYou have shipped AI features. The next step is owning the design and defending it
  • ML & data engineersYou know models. This is the systems layer around them that gets you the architect title
  • Consultants & pre-salesYou have to stand in front of a client and justify every box on the diagram

74 lessons, 8 modules, 6 weeks

One module a week, from vocabulary to a design you can defend, including a full module on AWS and a full module on Azure. Open any module to see every lesson inside it.

Lessons

  • What an AI Architect actually does: the six responsibilities and the artefact you ship for each
  • AI, ML, Deep Learning, GenAI: predictive vs generative, and choosing the simplest thing that works
  • Large Language Models: context windows, tokens, time to first token, TPM/RPM limits, hallucination risk
  • Embeddings and vector databases: chunking at scale, multi-tenancy, index freshness, re-embedding
  • RAG: Retrieval Augmented Generation, ingestion flow vs query flow
  • Adapting models: prompting vs RAG vs fine-tuning, and what each one really costs
  • Agents and agentic AI: when an agent beats a workflow, and when it does not
  • Model Context Protocol and standardised tooling
  • Evaluation: how you know it actually works
  • Cheat sheet and self-check

Lessons

  • The universal layered architecture: experience, gateway, orchestration, model, data, integration, platform
  • Reference Architecture 1: Enterprise RAG Assistant
  • Reference Architecture 2: Agentic Customer Service System
  • Reference Architecture 3: Document Intelligence Pipeline
  • Reference Architecture 4: Multi-Agent System
  • Non-functional requirements: the architect's edge
  • Security architecture for AI systems
  • How to answer a system design question live
  • Practice design briefs

Lessons

  • Reading the brief: users, volume, latency, budget, data, and what nobody said out loud
  • Turning requirements into non-functional requirements, and then into SLOs
  • Choosing the simplest approach that works: the decision tree
  • Drawing the seven layers for your brief, and marking build vs buy at each one
  • Choosing the model and the routing strategy, and writing down why
  • Designing the data path: chunking, index, freshness, tenancy
  • Placing the production controls: guardrails, cache, fallback, limits, tracing
  • Costing the design out loud, including the hidden calls
  • Writing the architecture decision record, and defending it

Lessons

  • The cloud fundamentals you need first: identity, secrets, private networking, queues, cache, monitoring
  • AWS ↔ Azure service map: what maps cleanly and what does not
  • How to answer ‘AWS or Azure?’ without picking a favourite
  • Orchestration frameworks: LangChain, LangGraph, LlamaIndex, OpenAI Agents SDK, Microsoft Agent Framework, Strands Agents, CrewAI
  • LLMOps tooling: OpenTelemetry tracing, evaluation, prompt management, model gateway, index versioning
  • Two production reference stacks, side by side

Lessons

  • The AWS AI and GenAI stack, service by service
  • Amazon Bedrock: model access, cross-Region inference, service tiers and prompt caching
  • Bedrock Knowledge Bases: ingestion, vector stores, retrieval and citation
  • Bedrock Guardrails: content policy, denied topics and contextual grounding checks
  • Bedrock AgentCore: Runtime, Gateway, Memory, Identity and Code Interpreter
  • SageMaker AI: where it still belongs in a GenAI architecture
  • The AWS platform layer: IAM, KMS, Secrets Manager, PrivateLink, WAF
  • Event-driven AI on AWS: SQS, Step Functions, Lambda and dead-letter queues
  • Observability and cost on AWS: CloudWatch, CloudTrail, request tagging, budgets
  • The AWS-native reference stack, drawn end to end

Lessons

  • The Azure AI and GenAI stack, service by service
  • Microsoft Foundry: projects, the model catalog and the Responses API
  • Azure OpenAI: deployments, quotas, provisioned throughput and content filters
  • Azure AI Search: hybrid search, semantic ranking and index design
  • Foundry Agent Service and Microsoft Agent Framework
  • Azure AI Content Safety and the responsible AI controls
  • The Azure platform layer: Entra ID, Key Vault, Private Link, Front Door and WAF
  • Event-driven AI on Azure: Service Bus, Azure Functions and Durable Functions
  • Observability and cost on Azure: Azure Monitor, Log Analytics, budgets and alerts
  • The Azure-native reference stack, drawn end to end

Lessons

  • AI gateways and model routing as a required architecture layer: virtual keys, quotas, caching, cost attribution
  • Context engineering: context assembly, compaction, retrieval budgets, tool-result pruning, sub-agent isolation
  • Beyond plain vector RAG: hybrid search and reranking as the baseline, then GraphRAG, agentic RAG and tiered routing
  • Agent memory architectures: working, episodic, semantic and procedural memory, plus compaction and decay
  • Agent sandboxing and secure code execution: container, gVisor and microVM isolation against the threat model
  • Agent identity and authorization: MCP as an OAuth 2.1 resource server, A2A signed Agent Cards, delegated authority
  • Inference economics: prompt and KV caching, quantization, speculative decoding, provisioned vs on-demand throughput
  • Model tiering: small language models, reasoning models and thinking budgets as a cost and latency lever
  • Multimodal and realtime voice: native speech-to-speech vs cascaded STT→LLM→TTS, and what each costs you
  • The compliance questions you will be asked: EU AI Act timelines, ISO/IEC 42001, NIST AI RMF, OWASP Top 10 for LLM Applications

Lessons

  • Why production AI is different
  • LLMOps: the operating model
  • Observability: what to instrument
  • Reliability and resilience patterns
  • Cost engineering
  • Governance, responsible AI and compliance
  • Incident response for AI systems
  • The interview question bank
  • Model answers to the five hardest questions
  • Your learning roadmap and final checklist
Layer 1: Common foundation for everyone

New to cloud or to AI? Start here. These four courses come with the program: take them at your own pace before week one, so the architecture work starts on level ground.

For everyone

Cloud for Beginners

What is cloud computing and why it matters, and 8 more core concepts.

Modules covered
Modules
  • What is cloud computing and why it matters
  • IaaS, PaaS, SaaS explained simply
  • AWS, Azure, GCP: what each is good for
  • Core cloud concepts: regions, availability zones, scalability
  • Cloud pricing models and cost basics
Labs
  • Lab: Create an AWS Free Tier account
  • Lab: Create an Azure Free Tier account
  • Lab: Install Visual Studio Code, Python and Git on a cloud VM
  • Lab: Install GitHub Copilot with VS Code
For everyone

AWS for Beginners

What is cloud computing and why AWS, and 8 more core concepts.

Modules covered
Modules
  • What is cloud computing and why AWS
  • EC2: instance types, AMIs, pricing models
  • S3: buckets, storage classes, lifecycle
  • RDS managed databases: MySQL, PostgreSQL, Aurora basics
  • IAM: users, groups, roles and permission policies
  • VPC: Virtual Private Cloud, subnets, security groups
  • AWS Pricing Calculator & Cost Explorer overview
Labs
  • Lab: Create an AWS Free Tier account
  • Lab: Set up billing alerts & budget controls
For everyone

Azure for Beginners

Azure portal, subscriptions and resource groups, and 11 more core concepts.

Modules covered
Modules
  • Azure portal, subscriptions and resource groups
  • Core Azure services: compute, storage, networking, databases
  • Microsoft Entra ID (Azure AD) and identity basics
  • Setting up your first Azure environment
  • Azure governance: policies, blueprints, management groups
Labs
  • Lab: Register an Azure free trial account
  • Lab: Create budget & billing alerts
  • Lab: Create a Windows VM (quick start)
  • Lab: Troubleshooting: connect to a VM on the cloud
  • Lab: Open an Azure support request
  • Lab: Requesting Azure service quota increases
  • Lab: Billing Q&A: identify and fix a storage bill
For everyone

AI, ML and GenAI for Beginners

What is AI, Machine Learning and Deep Learning?, and 9 more core concepts.

Modules covered
Modules
  • What is AI, Machine Learning and Deep Learning?
  • Generative AI and Large Language Models explained
  • Real-world AI use cases across industries
  • Introduction to Microsoft Foundry and Azure AI services
  • How models learn: training, inference and evaluation
Labs
  • Lab: Create API keys for the OpenAI GPT model
  • Lab: Create an API key for the Anthropic (Claude) model
  • Lab: Get started with GenAI & agents in Microsoft Foundry
  • Lab: Create a Weather API key (tool for agents)
  • Lab: Create a Serper API key (web search tool)
Three certification tracks, included

The credentials that back the title.

Architecture is judged on what you can defend, but the door still opens on what you can prove. Three certification tracks come with the program: two full courses and one focused exam-preparation kit. Sitting the exams themselves is separate, and the vendor exam fee is not included.

Claude Certified Architect Foundations badge

Claude Certified Architect: Foundations

Anthropic’s architect credential: agentic systems, MCP, tool use and multi-agent orchestration, ending in a full airline booking capstone.

CCAR-F · full course · 10 modules · 48 lessons · 25 labs
Lessons
  • AI for beginners
  • The AI Fluency 4D Framework
  • Four building blocks & when to reach for each
Labs
  • Lab 1.1: Environment setup & first API call
Lessons
  • Accessing Claude with the API
  • Prompt engineering fundamentals
  • Explicit criteria & reducing false positives
  • Few-shot prompting in depth
  • Measuring quality: prompt evaluation
Labs
  • Lab 2.1: Prompting drill: clarity, structure, few-shot
  • Lab 2.2: Structured output & a mini eval
Lessons
  • Tool use with Claude
  • Designing effective tool interfaces
  • Structured error responses
  • MCP fundamentals
  • Built-in tools & codebase understanding
  • MCP basics: clients, servers & primitives
  • MCP with Claude: transports & security
Labs
  • Lab 3.1: Differentiate and split tools
  • Lab 3.2: Structured error responses
  • Lab 3.3: Configure MCP servers
Lessons
  • The agentic loop lifecycle
  • Loop control & anti-patterns
  • Workflows vs agents
  • Task decomposition strategies
Labs
  • Lab 4.1: Build your first agentic loop
  • Lab 4.2: Decomposition drill
Lessons
  • CLAUDE.md hierarchy & modular rules
  • Skills, commands & permission modes
  • Plan mode vs direct execution & steering
  • CI/CD integration, automation & plugins
Labs
  • Lab 5.1: CLAUDE.md hierarchy & path-scoped rules
  • Lab 5.2: Skills, commands & plan mode
  • Lab 5.3: CI/CD integration
Lessons
  • Structured output via tool_use
  • Validation, retry & feedback loops
  • Batch processing at scale
  • Multi-instance & multi-pass review
Labs
  • Lab 6.1: Extraction & validation pipeline
  • Lab 6.2: Batch processing & multi-pass review
Lessons
  • Coordinator–subagent architecture
  • Subagent invocation, context passing & spawning
  • Tool distribution & tool_choice across agents
  • Enforcement: prerequisite gates & hooks
  • Structured handoff & session management
Labs
  • Lab 7.1: Coordinator with parallel subagents
  • Lab 7.2: Prerequisite gate & enforcement hooks
  • Lab 7.3: Structured handoff & sessions
Lessons
  • Long-context preservation
  • Escalation & ambiguity resolution
  • Error propagation across multi-agent systems
  • Large-codebase context & crash recovery
  • Confidence calibration & information provenance
Labs
  • Lab 8.1: Long-context preservation
  • Lab 8.2: Escalation & error propagation
  • Lab 8.3: Confidence & provenance
Lessons
  • Capstone system overview
  • Agentic architecture design
  • Flight search agent development
  • Booking validation agent
  • Customer communication agent
  • Production deployment architecture
Labs
  • Lab 9.1: Full system architecture design
  • Lab 9.2: Flight search agent implementation
  • Lab 9.3: Booking validation workflow
  • Lab 9.4: End-to-end system integration
Lessons
  • CCA-F exam format
  • Scenario-based preparation
  • Domain study strategy
  • Exam success approach
  • Pearson VUE exam setup
Labs
  • Exam Q&A: Claude Architect Foundations
  • Certification mock exam simulator
AWS Certified Solutions Architect Associate badge

AWS Certified Solutions Architect: Associate

The cloud architecture foundation under everything else: IAM, S3, EC2, VPC, databases, messaging and the Well-Architected Framework.

SAA-C03 · full course · 11 modules · 124 lessons · 36 labs
Lessons
  • Introduction to AWS Cloud (theory & labs)
Lessons
  • AWS Shared Responsibility Model: Part 1
  • AWS Shared Responsibility Model: Part 2
  • Identity and Access Management (IAM)
  • IAM components
  • IAM roles
  • IAM federation & SSO
  • IAM best practices
  • IAM delegation, audit, identity & credential management
  • AWS Organizations
  • Amazon Cognito
  • AWS WAF
  • AWS Shield
  • AWS GuardDuty & Firewall Manager
  • AWS Inspector
  • AWS KMS
  • AWS Certificate Manager
  • Accessing billing
Labs
  • Lab 1: Working with AWS IAM
  • Lab 2: Enable multi-factor authentication
  • Lab 3: AWS KMS create & use
  • Lab 4: IAM power user
Lessons
  • Object storage options
  • Simple Storage Service (S3)
  • S3 bucket policy
  • Amazon S3 ACL
  • S3 object versioning
  • Cross-region replication
  • Transfer acceleration in AWS S3
  • Storage classes
  • Requester pays, object locks
  • Content delivery network
  • AWS Snow Family
  • AWS Storage Gateway
  • S3 Glacier Vault & S3 Object Lock
  • S3 batch operations
  • AWS DataSync
  • Bucket-level security
Labs
  • Lab 1: Create S3 bucket, upload & access files
  • Lab 2: S3 cross-region replication
  • Lab 3: S3 lifecycle management on an S3 bucket
  • Lab 4: Deliver content faster with Amazon CloudFront
Lessons
  • Amazon EC2, tags
  • Metadata & user data
  • Amazon Machine Image
  • Key pairs in EC2
  • AWS security groups
  • EC2 hardware tenancy
  • EC2 purchasing options
  • Amazon EC2 networking layer
  • Amazon EC2 placement groups
  • AWS storage services
  • EBS snapshots
  • Amazon Elastic File System
  • SSM Session Manager
  • Amazon FSx
Labs
  • Lab 1: Configure web server IIS on a Windows EC2 machine
  • Lab 2: AWS EC2 instance metadata
  • Lab 3: Create & manage EBS volumes & snapshots
  • Lab 4: Attach & mount EBS volume to a Linux EC2 instance
  • Lab 5: Attach & mount EBS volume to a Windows EC2 instance
  • Lab 6: Create & mount EFS on an EC2 instance
  • Lab 7: Create Windows file share using Amazon FSx
Lessons
  • Elastic Load Balancer & its types
  • Comparison between the load balancers
  • Auto Scaling in AWS
  • Types of scaling
  • Lifecycle of Auto Scaling
  • Limitations of Auto Scaling
  • Introduction to AWS Route 53
  • Working of Route 53
Labs
  • Lab 1: Configure Network Load Balancer
  • Lab 2: Configure a load balancer & Auto Scaling
  • Lab 3: Launch template for an Auto Scaling group
  • Lab 4: Block web traffic with WAF in AWS
  • Lab 5: Register a GoDaddy domain & map using Route 53
Lessons
  • Introduction to VPC
  • Default & non-default VPC
  • Components of VPC
  • IP addressing
  • Classless Inter-Domain Routing
  • Subnets and subnet masks
  • IPv4 vs IPv6
  • Public & private subnet, internet gateway
  • VPN and Direct Connect
  • AWS CloudWatch
  • CloudTrail
  • AWS Trusted Advisor
  • AWS Config
  • Event-driven automated actions
  • CloudWatch & its working
  • What are monitoring services
  • Global Accelerator
Labs
  • Lab 1: Create a custom Virtual Private Cloud
  • Lab 2: Work with VPC peering connection
  • Lab 3: AWS Elastic IP
  • Lab 4: Configure Amazon CloudWatch
  • Lab 5: Enable CloudTrail and store logs in S3
  • Lab 6: Set AWS Config to assess, audit & evaluate
  • Lab 7: Establish a client-side VPN (4 parts)
  • Lab 8: Working with Transit Gateways
Lessons
  • Databases and their types
  • Amazon RDS and its benefits
  • RDS database instance and read replica
  • DB instance region and availability zones
  • Amazon DynamoDB
  • DynamoDB API and DynamoDB Streams
  • Read consistency in DynamoDB
  • Amazon ElastiCache
  • Amazon Redshift
  • Amazon Kinesis
  • Amazon Aurora
  • RDS & Aurora backups and security
  • Amazon DynamoDB global tables
  • DynamoDB benefits & use cases
  • Amazon DynamoDB Accelerator
  • Memcached & Redis
Labs
  • Lab 1: Create & query with Amazon DynamoDB
  • Lab 2: Configure a MySQL DB instance
  • Lab 3: Create a Redis cache & connect an EC2 instance
  • Lab 4: Amazon Athena
Lessons
  • AWS application services
  • AWS SES
  • AWS SNS
  • AWS SQS
  • Serverless computing
  • Amazon AppFlow
  • Amazon Pinpoint
  • Simple Workflow Service
  • Types of SQS
  • Limitations of SES
Labs
  • Lab 1: Send an email through AWS SES
  • Lab 2: Event-driven architectures
Lessons
  • Amazon CloudFormation
  • AWS Elastic Beanstalk
  • AWS OpsWorks
  • Elastic Beanstalk vs OpsWorks vs CloudFormation
  • YAML basics for Docker
  • YAML basics for Kubernetes
Labs
  • Lab 1: Create & update stacks using CloudFormation
  • Lab 2: Deploy a web application with Elastic Beanstalk
Lessons
  • Why do we need the Well-Architected Framework
  • How to build a Well-Architected Framework
  • Pillars of the Well-Architected Framework
  • Security pillar
  • Reliability pillar
  • Performance efficiency pillar
  • Cost optimization pillar
  • Operational excellence pillar
  • Resilience & how to calculate availability
  • Resources handling during failure
  • Resilience and databases
  • Decoupling services
  • Multi-tier architecture
  • Disaster recovery
  • Design performance architecture
Lessons
  • How to register for the AWS certification exam
  • Exam domains & weightings
  • Scenario-based question strategy
  • Full-length mock exams
Microsoft Certified Azure Solutions Architect Expert badge

Microsoft Azure Solutions Architect: Expert

A focused preparation kit rather than a full course: Azure fundamentals, the AZ-305 design domains, architecture case studies and four practice exam sets with live mock simulations.

AZ-305 · exam preparation kit · 14 modules · 62 lessons · 19 labs
Lessons
  • What is Active Directory
  • Understand Microsoft Entra ID (Azure AD)
  • What is PHS and PTA
  • What is Active Directory Federation Services
Labs
  • Lab: Entra ID (Azure AD) sync with on-premises AD
  • Lab: User-assigned managed identities for Azure
Lessons
  • Virtual networks
  • ARM templates
  • What is role-based access control (RBAC)
  • Policies & initiatives
  • PIM (Privileged Identity Management)
  • Network security groups
  • Managed identity and service principal
  • Azure Blueprints
Labs
  • Lab: Role-based access control (RBAC) in Azure
  • Lab: Implementing custom role-based access
  • Lab: Governance with Azure Blueprints & Policy
Lessons
  • Difference between IaaS, PaaS and Azure App Service
  • App Service pricing and ACU
  • AKS cluster
  • Container images and container instances
Labs
  • Lab: Create a virtual machine: Windows
  • Lab: Create a VM from a Linux image
  • Lab: Create a virtual machine with a custom image
  • Lab: How to encrypt a virtual machine disk
  • Lab: Deploying & testing Virtual Machine Scale Sets
  • Lab: Implement Azure Container Instance (ACI)
  • Lab: Deploy an Azure Kubernetes Service (AKS) cluster
Lessons
  • Difference between VNet peering, VPN Gateway and ExpressRoute
  • VNet peering
  • VPN Gateway
  • ExpressRoute
  • Load balancer
  • Application Gateway
  • Traffic Manager
  • Content delivery network
  • Azure Front Door
Labs
  • Lab: Network Watcher in Azure
Lessons
  • Services in a storage account
  • Replication options
  • Cloud-agnostic model
  • Key Vault
Labs
  • Lab: Manage Azure storage accounts and access
Lessons
  • File share, file sync & sync groups
  • Online Data Box
  • Offline Data Box
  • Backup
Lessons
  • Synapse Link, Stream Analytics & Azure Search
  • Cloud migration scenarios
Lessons
  • Recommend an app configuration management solution
  • FAQ on application architecture
Lessons
  • Azure Monitor
  • Network Watcher & Log Analytics
  • Operational monitoring services
  • Differences between Microsoft Defender plans
Labs
  • Lab: Exploring the monitoring capabilities of VMSS
Lessons
  • Backup and recovery design
  • Site recovery planning
Labs
  • Lab: VM replication via Azure Site Recovery
Lessons
  • Why we need migration & landing zones
  • Overview of Azure Migrate
  • VMware agentless migration
  • VMware agent-based migration
  • Planning an Azure migration
  • On-premises to Azure migration
  • Assessment using Azure Migrate
  • Migrate servers with Azure Migrate
  • Migrate databases with Azure Database Migration Service
  • Azure Database Migration
  • Migrate on-premises data to cloud storage
  • TCO (Total Cost of Ownership)
Labs
  • Lab: Azure resource migration across regions
Labs
  • Lab: Common Azure issues and fixes
Lessons
  • Azure database migration case study
  • Building a resilient IaaS architecture
  • Building a microservice architecture
Lessons
  • Practice exam Q&A set 1
  • Practice exam Q&A set 2
  • Practice exam Q&A set 3
  • Practice exam Q&A set 4
Labs
  • Live simulation mock sets 1, 2 & 3

One blueprint, seven layers

Every AI system you will be asked to design fits this shape. What changes is which layers you build, which you buy, and what control you put at each one, so each layer here is given with its AWS and Azure services side by side.

Layer 1Experience

Chat, search, an embedded copilot or a batch job. Where the user meets the system.

Production controlStreaming, citations, graceful degradation and a visible path to a human.

CloudFront · S3 · AmplifyFront Door · Static Web Apps · App Service
Layer 2Gateway

Authentication, rate limits, quotas, guardrails, caching and routing. Your policy enforcement point.

Production controlInput and output guardrails, per-tenant quotas, semantic cache, cost attribution by request tag.

API Gateway · WAF · Bedrock GuardrailsAPI Management · WAF · AI Content Safety
Layer 3Orchestration

Workflows, agents, tool calls and the control flow between them.

Production controlStep limits, timeouts, runaway guards, durable state, sandboxed code execution.

Bedrock AgentCore · Step Functions · LambdaFoundry Agent Service · Agent Framework · Durable Functions
Layer 4Model

Model choice, routing, fallback and the thinking budget you allow.

Production controlA model gateway, a fallback model and fallback Region, prompt caching, token budgets.

Amazon Bedrock · SageMaker AIAzure OpenAI · Foundry model catalog
Layer 5Data

Chunking, embeddings, indexes, retrieval and agent memory.

Production controlIndex versioning, freshness and re-embedding, tenant isolation, retrieval evaluation.

Bedrock Knowledge Bases · S3 Vectors · OpenSearchAzure AI Search · Cosmos DB · Blob Storage
Layer 6Integration

Systems of record, tools, queues and other agents.

Production controlIdempotency, retries with back-off, dead-letter queues, back-pressure, tool-level authorization.

AgentCore Gateway (MCP) · SQS · EventBridgeService Bus · Logic Apps · Event Grid
Layer 7Platform

Identity, secrets, private networking, tracing, evaluation and CI/CD.

Production controlOne trace per request, evaluation gates in CI, budgets and anomaly alerts, threat model and runbooks.

IAM · KMS · PrivateLink · CloudWatchEntra ID · Key Vault · Private Link · Azure Monitor

The system design method

Nine steps that turn a blank brief into a defended design. You practise them against timed briefs until the answer comes out structured under pressure, which is exactly what a design interview is testing.

  1. 01Read the briefUsers, volume, latency, budget, data, and what nobody said out loud.
  2. 02Name the NFRsTurn vague requirements into non-functional requirements, then into SLOs with numbers.
  3. 03Pick the simplest approachRules, predictive ML, one LLM call, RAG, a workflow or an agent. In that order.
  4. 04Draw the seven layersLay the blueprint over the brief and mark what you build and what you buy.
  5. 05Choose the modelModel, routing strategy, fallback, and the reason you will be asked for.
  6. 06Design the data pathChunking, index, freshness, tenancy, and how a citation gets back to a source.
  7. 07Place the controlsGuardrails, cache, fallback, step and cost limits, one trace per request.
  8. 08Cost it out loudPer request and per month, including the hidden calls: guardrails, rewrite, rerank, judge.
  9. 09State risk and next stepsThe trade-offs you accepted, what would break first, and what you would do next.

4 reference architectures

The four designs that come up again and again, in interviews and in client rooms. Each one is given as a cloud-neutral pattern first, then drawn service by service on AWS and on Azure. Click any card to flip it and see the full diagram.

What you walk away with

Not notes. The six artefacts an architect is actually judged on.

An architecture diagram

The 7-layer blueprint applied to a real brief, with the production control named at every layer.

Architecture decision records

Why this model, this retrieval strategy, this cloud: written down so it survives you leaving the room.

SLOs and a cost model

Non-functional requirements turned into numbers, including the hidden calls: guardrails, rewrite, rerank, judge.

A threat model

Defence in depth across seven control layers, mapped to the OWASP Top 10 for LLM Applications.

Runbooks

What you do at 3am when the assistant gives dangerously wrong information: rehearsed, not improvised.

A defended design

The nine-step method, practised against timed briefs, until the answer comes out structured.

What AI architecture postings actually ask for

Share of AI architecture job postings mentioning each skill. Note where observability sits: it is asked for more often than RAG, and almost no curriculum teaches it properly.

  • Cloud platforms (AWS, Azure, GCP)63%
  • Python44%
  • Azure42%
  • AWS42%
  • Foundation models40%
  • Observability & monitoring38%
  • CI/CD28%
  • RAG28%
  • Databricks16%
  • Docker13%

Based on analysis of ~16,900 AI architecture postings: median salary $189,000, median experience 7 years, postings up 24% quarter on quarter.
Market data, not a K21 guarantee: your outcome depends on your experience and your market.

Roles this opens up

One skill set, a dozen job titles. Outcomes depend on your effort, your prior experience and the market.

  • AI Architect
  • GenAI Solutions Architect
  • Agentic AI Architect
  • AI Solution Architect
  • Cloud Architect, AI
  • AI Platform Engineer
  • LLMOps Engineer
  • AI Observability Lead
  • AI Security Architect
  • Principal AI Engineer
  • AI Technical Lead
  • AI Pre-Sales Architect
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Rated 4.6/5 on Trustpilot · 267+ reviews
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✓Devesh Azure Solutions Architect
✓Poulami Beginner to Cloud Architect
✓Kennedy Senior Technical Advisor (UK)
✓SJ Salary doubled, husband hired too
✓Shouvik Senior Enterprise Architect
✓Meghana Career gap to working with BT
✓Kwame Job offer, relocated to USA
✓Rajalakshmi Career gap to Job at Infosys
✓Winifred Job offer in Canada
✓Bezzel Cloud Ops Engineer
In their own words

From our learners who landed the role.

Verified reviews from Google and Trustpilot. Every profile links to the real person on LinkedIn.

“I'm thrilled to share that I've recently landed a role as a Generative AI Engineer.” The hands-on projects let me confidently describe an end-to-end AI solution in the interview. The interviewer was genuinely impressed.
SteveGenerative AI Engineer✓ Google review verifiedin
“I've successfully landed a job as an AI Engineer.” Worked hands-on with Azure AI services, Azure AI Foundry, and OpenAI. Later received a second offer as a Generative AI Engineer. K21 gave me the skills and confidence to choose.
Debasish DashAI Engineer to Generative AI Engineer✓ Google review verifiedin
“Enrolling in K21 Academy's AI/ML program has been one of the best professional decisions I've made this year.” As a VP of Software Engineering transitioning into AI/ML, I completed four courses and passed three Microsoft certification exams: AI-900, AZ-900, and AB-731. The hands-on projects on Azure gave me practical experience that actually shows up in interviews.
Marcos OliveiraVP of Software Engineering✓ Trustpilot verifiedin
“I have finally secured a contract-to-hire role as a Generative AI Engineer.” I was deploying a RAG pipeline to a web app during the actual hiring process. The projects I built at K21 were exactly what the company needed.
Ike ImalaGenerative AI Engineer (contract-to-hire)✓ Google review verifiedin
“I got cracked that role.” The Azure AI/ML trainee landed the job after completing the program and building out her portfolio.
SemantiAzure AI/ML Engineer✓ Google review verifiedin
“K21 Academy is a great professional institution to meet your AI/ML career aspirations.” The right training, courseware, job application support, and placement guidance to help students achieve their goals. I particularly like the quality and depth of the faculty's knowledge.
Sanjeev SaksenaAI/ML Career Transition✓ Google review verifiedin
“The support is extremely awesome for labs, doubts and even resume updates.” I am 3 weeks into the AI/ML program. The team creates a custom game plan and learning path for everyone. The real-world project experiences and hands-on lab experience is a key differentiator.
Gaj ParanjapeAI/ML Program, USA✓ Trustpilot verifiedin
“I joined K21 Academy at a time when I felt stuck and unsure about my career direction.” Connecting with Shahid was a turning point. He gave me clarity, practical guidance, and renewed confidence. The AI curriculum is exhaustive and serves as a one-stop shop for AI and ML.
Gunita BajajFull Stack AI Program, USA✓ Trustpilot verifiedin
“Excited to share that I have successfully passed the Microsoft AI-900 certification!” Credits K21 Academy guidance for the preparation and is now moving on to the next Azure AI certification.
Adnan AhmedAI Engineer - AI-900 Certified✓ Trustpilot verifiedin
“I decided to explore AI as part of a career change, but coming from a non-technical background made me hesitant.” I joined K21 Academy and I'm really glad I did. The course is well-structured, catering to both beginners and advanced learners. The emphasis on hands-on experience truly enhances the learning process.
Monica VarryAI Career Changer✓ Google review verifiedin
“I've secured a position as an OpenShift Architect.” Credits the structured training and support system for making the transition into a cloud architecture role.
Stephen AgborOpenShift Architect✓ Trustpilot verifiedin
“The structured curriculum, supportive mentors, and proper guidance have given me a lot of clarity.” When I first started learning AI and Azure, I was quite confused about where to begin. K21's structured approach gave me direction and I now feel more confident in my learning journey.
Rahul SyalAI/ML Learner, India✓ Trustpilot verifiedin

One program. Two plans.

Both plans include everything above - 6 weeks, 74 lessons, 4 reference architectures on both clouds, and a year of access. Job preparation adds the resume, the mock interviews and the placement support on top.

Best value
Upskill
$2,997

One payment.

  • 6-week structured program, one module per week
  • 74 lessons across 8 modules
  • The universal 7-layer architecture blueprint, mapped to AWS and Azure
  • 4 reference architectures - as a pattern, on AWS and on Azure
  • A dedicated AI Architect on AWS module
  • A dedicated AI Architect on Azure module
  • The nine-step method for a live system design question
  • Timed practice design briefs
  • The interview question bank + model answers to the five hardest questions
  • Incident response scenarios to rehearse
  • 1-year access to all materials and recordings
  • Live Q&A and query support throughout
Enroll in Upskill - $2,997

Six-month money-back guarantee

Flexible
Upskill
$1,097 × 3

Three monthly payments of $1,097, $3,291 in total.

  • 6-week structured program, one module per week
  • 74 lessons across 8 modules
  • The universal 7-layer architecture blueprint, mapped to AWS and Azure
  • 4 reference architectures - as a pattern, on AWS and on Azure
  • A dedicated AI Architect on AWS module
  • A dedicated AI Architect on Azure module
  • The nine-step method for a live system design question
  • Timed practice design briefs
  • The interview question bank + model answers to the five hardest questions
  • Incident response scenarios to rehearse
  • 1-year access to all materials and recordings
  • Live Q&A and query support throughout
Enroll - 3 × $1,097

Six-month money-back guarantee

Most popular
Upskill + job preparation
$3,997

One payment.

  • 6-week structured program, one module per week
  • 74 lessons across 8 modules
  • The universal 7-layer architecture blueprint, mapped to AWS and Azure
  • 4 reference architectures - as a pattern, on AWS and on Azure
  • A dedicated AI Architect on AWS module
  • A dedicated AI Architect on Azure module
  • The nine-step method for a live system design question
  • Timed practice design briefs
  • The interview question bank + model answers to the five hardest questions
  • Incident response scenarios to rehearse
  • 1-year access to all materials and recordings
  • Live Q&A and query support throughout
  • Everything above, plus
  • AI architect resume, rebuilt and reviewed
  • LinkedIn profile rebuilt and optimised
  • Mock system design interviews with structured feedback
  • Coaching through the interview question bank and the model answers
  • Job-search strategy, application tracking and a monthly job clinic
  • 1-month on-job support after placement
Enroll in Upskill + Job Prep - $3,997

Six-month money-back guarantee

Flexible
Upskill + job preparation
$1,097 × 4

Four monthly payments of $1,097, $4,388 in total.

  • 6-week structured program, one module per week
  • 74 lessons across 8 modules
  • The universal 7-layer architecture blueprint, mapped to AWS and Azure
  • 4 reference architectures - as a pattern, on AWS and on Azure
  • A dedicated AI Architect on AWS module
  • A dedicated AI Architect on Azure module
  • The nine-step method for a live system design question
  • Timed practice design briefs
  • The interview question bank + model answers to the five hardest questions
  • Incident response scenarios to rehearse
  • 1-year access to all materials and recordings
  • Live Q&A and query support throughout
  • Everything above, plus
  • AI architect resume, rebuilt and reviewed
  • LinkedIn profile rebuilt and optimised
  • Mock system design interviews with structured feedback
  • Coaching through the interview question bank and the model answers
  • Job-search strategy, application tracking and a monthly job clinic
  • 1-month on-job support after placement
Enroll - 4 × $1,097

Six-month money-back guarantee

Before you enroll

No, and that is the point. You will read and draw architecture, not write training loops. The work is choosing between options, naming the trade-off, sizing the cost and defending the decision - which is what the role is actually assessed on. You should be comfortable in a technical conversation; you do not need to be the strongest coder in the room.

Module 1 is vocabulary, and you may move through it quickly. The value starts at Module 2 and 3: the 7-layer blueprint, four reference architectures, and the step-by-step method for turning a blank brief into a defended design. Most engineers who build AI have never had to defend an architecture to a panel, and that is the gap this closes.

6 weeks, with 74 lessons across 8 modules. Each module is mapped to a week so you always know where you should be, and you keep access to everything for a full year.

No. Module 4 starts with the cloud fundamentals AI systems depend on - identity, secrets, private networking, queues, API front door, cache, monitoring. Modules 5 and 6 then go deep on each cloud separately, so you can design on either one and answer ‘AWS or Azure?’ with a reason rather than a preference.

It is the spine of the program. Module 2 covers how to answer a system design question live, Module 3 is the nine-step method applied end to end, and you work through timed practice design briefs. Every reference architecture is given as a pattern first, then drawn on AWS and on Azure, so you can redraw it under pressure.

It is maintained against what is actually shipping. Module 7 covers the AI gateway layer, context engineering, agent memory and sandboxing, agent identity and authorization, inference economics, model tiering and the current compliance timelines. Platform naming is kept current too - Bedrock AgentCore rather than the older Bedrock Agents, and Microsoft Foundry rather than Azure AI Foundry.

LangChain and LangGraph, LlamaIndex, the OpenAI Agents SDK, Microsoft Agent Framework, Strands Agents and CrewAI - with an honest read on where each one fits and where teams outgrow it. On the operations side: OpenTelemetry tracing, evaluation tooling, prompt management, model gateways and index versioning.

AI architect, GenAI solutions architect, agentic AI architect, AI solution architect, AI platform engineer, LLMOps engineer and AI security architect. Architecture postings skew senior - the median experience asked for is around seven years - so this suits people already working in technology rather than complete beginners.

Every lesson, the slide decks, the reference architecture diagrams in all three variants, the practice design briefs, the interview question bank and the model answers. Access runs for a full year.

Upskill is the full program - every module, every reference architecture, the practice briefs and a year of access. Upskill + job preparation adds the career side on top: your resume rebuilt for architect roles, your LinkedIn profile optimised, mock system design interviews with structured feedback, coaching through the question bank, job-search strategy with a monthly job clinic, and on-job support once you are placed.

Yes, on both plans. Upskill is $2,997 in full or three monthly payments of $1,097. Upskill + job preparation is $3,997 in full or four monthly payments of $1,097. Paying in full is the cheaper route on either plan.

Be the one who can defend the design

Learn the blueprint, draw 4 reference architectures on both clouds, and walk into the next design conversation with a method.

See pricing and enroll
AI Architect & System Design Program · K21 Academy