Level: AI Infrastructure Architect (Role Level 3) Focus: Enterprise Architecture Artifacts & Reference Implementations Total Learning Hours: 425 hours Prerequisites: Completion of Senior AI Infrastructure Engineer level
This repository contains comprehensive architecture solutions for the AI Infrastructure Architect role. Unlike engineer-focused repos with primarily code, this repository emphasizes:
- 60% Architecture Artifacts: Designs, ADRs, business cases, governance frameworks
- 40% Reference Implementations: Code that validates architectural decisions
| Aspect | Engineer/Senior Engineer Repos | Architect Repo (This One) |
|---|---|---|
| Primary Focus | Working code implementation | Architecture artifacts and decisions |
| Documentation | How-to guides, API docs | Business cases, ADRs, stakeholder presentations |
| Scope | Single system/service | Enterprise platforms, multi-year strategies |
| Audience | Technical teams | C-suite, architects, technical leads |
| Success Metrics | System performance, uptime | Business value, ROI, strategic alignment |
| Artifacts | Code, tests, deployment configs | C4 diagrams, financial models, governance frameworks |
ai-infra-architect-solutions/
├── projects/ # 5 comprehensive architecture projects
│ ├── project-301-enterprise-mlops/
│ ├── project-302-multicloud-infra/
│ ├── project-303-llm-rag-platform/
│ ├── project-304-data-platform/
│ └── project-305-security-framework/
├── architecture-templates/ # Reusable templates for architecture work
│ ├── architecture-decision-records/
│ ├── design-documents/
│ ├── business-cases/
│ └── stakeholder-presentations/
├── frameworks/ # Comprehensive frameworks
│ ├── security-compliance/
│ ├── cost-optimization/
│ ├── ha-dr/
│ └── governance/
└── guides/ # In-depth guides (11,500+ lines)
├── architecture-patterns.md (4,000+ lines)
├── enterprise-standards.md (3,000+ lines)
├── stakeholder-communication.md (2,500+ lines)
└── cost-benefit-analysis.md (2,000+ lines)
Business Challenge: Design a scalable, governed MLOps platform supporting 100+ data scientists across 20+ teams, with full model lifecycle management, compliance, and multi-tenancy.
Key Deliverables:
- Complete C4 architecture diagrams (Context, Container, Component, Deployment)
- 10+ Architecture Decision Records
- Business case with 3-year ROI analysis ($15M investment, $45M value)
- Model governance framework
- Stakeholder presentations (executive, technical, operational)
- Reference Terraform/K8s implementation
Technologies: Kubernetes, Kubeflow, MLflow, Feature Store (Feast/Tecton), Multi-cloud (AWS/GCP/Azure)
Business Value: $30M NPV, 35% cost reduction, 60% faster model deployment
Key Decisions:
- Feature store technology selection (built vs buy)
- Model registry approach (centralized vs federated)
- Multi-tenancy design (namespace vs cluster isolation)
- Governance framework (automated vs manual approval)
Business Challenge: Architect a multi-cloud AI infrastructure spanning AWS, GCP, and Azure with data sovereignty compliance, disaster recovery (RTO<1hr, RPO<15min), and cost optimization.
Key Deliverables:
- Multi-cloud vendor selection framework
- Architecture for 3 clouds (AWS, GCP, Azure) with detailed comparison
- HA/DR plan with RTO/RPO analysis and runbooks
- Data sovereignty compliance framework (GDPR, CCPA, regional laws)
- FinOps cost optimization strategy
- Migration strategy with phased rollout plan (18 months)
- Reference Terraform multi-cloud implementation
Technologies: Terraform, Crossplane, Kubernetes Federation, Cloud-native services (EKS, GKE, AKS)
Business Value: 99.95% uptime, $8M annual cost savings, regulatory compliance across 15 countries
Key Decisions:
- Cloud vendor strategy (best-of-breed vs primary+secondary)
- Data residency architecture (regional data lakes)
- Disaster recovery approach (active-active vs active-passive)
- Cost optimization strategy (reserved vs spot vs on-demand)
Business Challenge: Design enterprise LLM platform serving 10,000+ users with RAG capabilities, responsible AI governance, cost optimization ($500K → $150K/month), and safety guardrails.
Key Deliverables:
- LLM infrastructure architecture (GPU clusters, inference optimization)
- Model selection framework with evaluation criteria (20+ LLMs evaluated)
- Complete RAG system design with vector database architecture
- LLM safety and governance framework (bias, toxicity, hallucination mitigation)
- Cost-performance optimization analysis (70% cost reduction achieved)
- Reference vLLM/TensorRT-LLM implementation
- Responsible AI compliance framework
Technologies: vLLM, TensorRT-LLM, Vector DB (Pinecone/Weaviate), LangChain, GPU clusters (A100/H100)
Business Value: $4.2M annual cost savings, 10x throughput improvement, enterprise compliance
Key Decisions:
- LLM deployment strategy (self-hosted vs managed)
- Vector database selection (cost vs performance vs features)
- RAG architecture (single-stage vs multi-stage retrieval)
- Safety framework (rule-based vs ML-based guardrails)
Business Challenge: Architect a unified data platform supporting both batch and real-time ML workloads, processing 100TB+ daily, with data governance, quality, and feature engineering at scale.
Key Deliverables:
- Data lakehouse architecture (Delta Lake/Iceberg/Hudi comparison and selection)
- Real-time streaming architecture (Kafka, Flink) handling 10M events/sec
- Data governance framework (catalog, lineage, quality, privacy)
- ML platform integration design (feature store, model training)
- Feature engineering platform architecture
- Reference lakehouse implementation with Databricks/Snowflake comparison
- Data quality framework with automated monitoring
- Privacy and compliance design (differential privacy, access controls)
Technologies: Delta Lake/Iceberg, Kafka, Spark, Airflow, dbt, Data Catalog (Datahub/Amundsen)
Business Value: 50% reduction in data engineering time, 99.9% data quality, compliance readiness
Key Decisions:
- Lakehouse format selection (Delta vs Iceberg vs Hudi)
- Streaming platform architecture (Kafka vs Kinesis vs Pub/Sub)
- Data governance approach (centralized vs federated)
- Feature store integration (build vs buy)
Business Challenge: Create comprehensive security architecture for ML platform in regulated industry (healthcare/finance), achieving SOC2, HIPAA, and ISO27001 compliance.
Key Deliverables:
- Zero-trust architecture design for ML platform
- Comprehensive compliance framework (GDPR, HIPAA, SOC2, ISO27001)
- ML-specific security considerations (model security, adversarial defenses)
- IAM architecture with fine-grained access control
- Encryption strategy (at rest, in transit, in use - including confidential computing)
- Incident response framework with runbooks
- Security monitoring and SIEM architecture
- Reference Kubernetes security implementation
- Compliance checklists and audit procedures (200+ controls)
Technologies: Kubernetes security, HashiCorp Vault, Cloud KMS, SIEM (Splunk/Elastic), Confidential Computing
Business Value: Compliance certification achieved, 85% reduction in audit time, zero security incidents
Key Decisions:
- Zero-trust implementation approach (service mesh vs native)
- Secrets management (Vault vs cloud-native)
- Encryption strategy (performance vs security trade-offs)
- Compliance framework (build vs compliance-as-code platforms)
By completing this repository, you will:
- ✅ Design enterprise-scale AI/ML platforms supporting 100+ teams
- ✅ Create comprehensive C4 architecture diagrams
- ✅ Write effective Architecture Decision Records (ADRs)
- ✅ Develop multi-year technology roadmaps
- ✅ Perform vendor selection with structured evaluation frameworks
- ✅ Design for 99.95%+ uptime with HA/DR strategies
- ✅ Build compelling business cases with ROI analysis (NPV, TCO, payback period)
- ✅ Conduct cost-benefit analysis for $10M+ investments
- ✅ Translate technical architecture to executive language
- ✅ Perform risk assessment and mitigation planning
- ✅ Create stakeholder-specific presentations (board, C-suite, technical)
- ✅ Demonstrate measurable business value ($50M+ impact)
- ✅ Design model governance frameworks
- ✅ Architect for regulatory compliance (GDPR, HIPAA, SOC2, ISO27001)
- ✅ Implement responsible AI and ethical AI frameworks
- ✅ Create data governance and lineage systems
- ✅ Design security architectures (zero-trust)
- ✅ Establish architecture governance processes
- ✅ Lead multi-cloud and hybrid architecture initiatives
- ✅ Drive cost optimization strategies ($5M+ annual savings)
- ✅ Design disaster recovery and business continuity plans
- ✅ Create FinOps frameworks and cost allocation models
- ✅ Manage strategic partnerships and vendor relationships
- ✅ Balance build vs buy vs partner decisions
Recommended Path:
- Start with LEARNING_GUIDE.md to understand how architects learn differently
- Review architecture-templates/ to understand standard artifacts
- Study Project 301 in detail (start with README → business case → architecture diagrams → ADRs)
- Attempt to create your own version before reviewing reference implementation
- Compare your design decisions with the provided ADRs
- Progress through remaining projects
Time Investment:
- Browsing: 20 hours
- Studying: 100 hours
- Applying to your context: 300+ hours
Usage:
- Use projects as case studies for architecture workshops
- Assign students to critique architecture decisions
- Have teams debate alternative approaches in ADRs
- Use stakeholder presentations as templates
- Leverage business cases for ROI analysis exercises
Focus Areas:
- Study ADRs to understand decision-making frameworks
- Review cost analysis methodologies
- Practice explaining architecture to different audiences
- Use C4 diagrams as examples of effective communication
- Memorize key metrics and business value statements
Interview Questions Covered:
- "Design an enterprise MLOps platform for 500 data scientists"
- "How would you architect a multi-cloud AI infrastructure?"
- "What's your approach to LLM cost optimization?"
- "How do you ensure compliance in ML systems?"
- "Explain your HA/DR strategy for mission-critical ML"
Adapt Projects:
- Customize business cases for your industry
- Modify architecture for your org size/maturity
- Create your own ADRs for decisions you've made
- Build your own C4 diagrams for your systems
- Document your ROI and business value achieved
Showcase:
- Include architecture diagrams in presentations
- Reference ADR methodology in interviews
- Share cost optimization results with metrics
- Demonstrate stakeholder communication skills
- 15-20 Architecture Documents per project
- 10+ ADRs (Architecture Decision Records) per project
- Complete Business Cases with financial models
- Stakeholder Presentations (executive, technical, operational)
- Governance Frameworks with policies and procedures
- Reference Implementations validating architecture
- ADR template with examples
- Design document template
- Business case template with financial models
- Stakeholder presentation templates
- Risk assessment template
- RFP response template
- Security compliance framework (200+ controls)
- Cost optimization framework with calculators
- HA/DR framework with RTO/RPO templates
- Governance framework with review processes
- Architecture patterns for enterprise systems
- Enterprise standards and conventions
- Stakeholder communication strategies
- Cost-benefit analysis methodologies
- Kubernetes (advanced operators, multi-cluster)
- Terraform (multi-cloud IaC)
- Crossplane (cloud-agnostic control plane)
- Kubeflow (ML platform)
- Airflow (workflow orchestration)
- MLflow (experiment tracking, model registry)
- Feature Stores (Feast, Tecton, SageMaker)
- Model Serving (KServe, Seldon, TensorRT-LLM)
- vLLM (LLM serving)
- LangChain/LlamaIndex (LLM orchestration)
- Delta Lake / Apache Iceberg / Apache Hudi
- Apache Kafka (streaming)
- Apache Spark (batch processing)
- dbt (data transformation)
- Data Catalogs (Datahub, Amundsen)
- AWS (EKS, SageMaker, S3, Bedrock)
- GCP (GKE, Vertex AI, BigQuery)
- Azure (AKS, Azure ML, Synapse)
- Zero-trust architectures
- HashiCorp Vault (secrets management)
- SIEM platforms (Splunk, Elastic)
- Compliance frameworks (GDPR, HIPAA, SOC2, ISO27001)
- Prometheus & Grafana
- Distributed tracing (Jaeger, Tempo)
- Log aggregation (ELK stack)
- Cost monitoring (Kubecost, Cloud Cost Management)
Learners who master this repository will be able to:
| Capability | Target |
|---|---|
| Design enterprise platforms | Supporting 100+ teams, 500+ models |
| Business value delivery | $50M+ NPV, 30%+ cost reduction |
| System availability | 99.95%+ uptime |
| Compliance achievement | SOC2, HIPAA, ISO27001 certified |
| Stakeholder satisfaction | Executive approval for $10M+ initiatives |
| Cost optimization | $5M+ annual savings |
| Team leadership | Lead 10+ architects/senior engineers |
| Industry recognition | Published articles, conference talks |
This repository prepares you for:
Current Role: AI Infrastructure Architect
- Senior-level IC role at Big Tech (L6/L7)
- Director of ML Infrastructure
- Principal Engineer, ML Platform
- Architecture Lead, AI/ML
Next Role: Senior AI Infrastructure Architect (Level 4)
- Distinguished Engineer
- VP of ML Infrastructure
- Chief Architect, AI
- CTO/VP Engineering (AI-focused startups)
Salary Range (US, 2025):
- Base: $200K - $300K
- Total Comp: $350K - $600K (with equity at Big Tech)
- Consulting: $250 - $500/hour
Before starting this repository, you should have:
✅ Completed Senior AI Infrastructure Engineer level (or equivalent 5-8 years experience) ✅ Led design of production ML systems supporting 10+ teams ✅ Hands-on experience with Kubernetes, cloud platforms, and MLOps tools ✅ Exposure to multi-stakeholder projects (working with Product, Business, Legal) ✅ Some understanding of business metrics (revenue, cost, ROI) ✅ Desire to transition from building to designing systems
Not Required (you'll learn these):
- TOGAF certification (covered in curriculum)
- Executive communication experience
- Multi-cloud architecture experience
- Formal business training
- Browsing all projects: 20 hours
- Deep study of all artifacts: 100 hours
- Completing all projects: 425 hours (as per curriculum)
- Mastery with real-world application: 1000+ hours
Recommended Schedule:
- Full-time: 10-12 months (working through curriculum)
- Part-time (20 hrs/week): 20 months
- Self-paced: Review individual projects as needed
We welcome contributions! See CONTRIBUTING.md for guidelines.
Especially valuable:
- Real-world case studies (anonymized)
- Updated cost models with current pricing
- Alternative architecture patterns
- Lessons learned from production deployments
- Compliance framework updates
MIT License - see LICENSE for details.
- GitHub Issues: Questions, bugs, suggestions
- Email: ai-infra-curriculum@joshua-ferguson.com
- Organization: github.com/ai-infra-curriculum
This curriculum was designed based on:
- Real-world architecture practices from Fortune 500 companies
- TOGAF framework and enterprise architecture best practices
- Industry standards (AWS Well-Architected, Google Cloud Architecture Framework)
- Interviews with 20+ AI Infrastructure Architects from leading tech companies
- Research papers and publications on ML infrastructure at scale
Ready to become an AI Infrastructure Architect? Start with LEARNING_GUIDE.md to understand the architect mindset, then dive into Project 301: Enterprise MLOps Platform.