GenAI Solutions Architecture
Design enterprise GenAI reference architectures, create ADRs and technical standards, bridge GenAI with enterprise workflows.
Verifiable skill graph
12 skill groups · each becomes a signed node on your graph.
Verifiable skill graph
12 skill groups · each becomes a signed node on your graph.
Every lab you pass signs a W3C Verifiable Credential on your public skill graph. Completing the labs in each group below mints one node on that graph — the badge you walk away with is a cryptographic record of what you can ship, not a completion certificate.
Share the URL on your résumé or with a hiring manager. They click; they see the discipline, the labs you passed, and the verification signature. No honor system, no broker.
Production-grade Python applied to architectural artifacts: async/await, Pydantic models for ADRs + arch registries, typing, dataclasses, pytest for arch review automation.
Provider SDK calls in architecture-review tooling: OpenAI/Anthropic/Gemini integration, LiteLLM gateways at platform level, multi-provider abstraction for architectural resilience.
ADR engines, reference architecture registries, architecture review automation, technical standards documentation, architecture decision capture + lineage.
Compound AI system designers, event-driven AI processors, multi-modal pipelines, streaming AI pipelines, context engineering platforms, architectural pattern libraries.
Cost-optimized AI routers, AI traffic gateways, provider reliability engines, multi-provider abstraction patterns, smart routing strategies, provider failover at scale.
AI data architecture, enterprise AI integration hubs, data infrastructure foundations, RAG-at-scale data architecture, event sourcing + CDC patterns for AI.
Cell-based AI platform architecture, multi-tenant AI platforms, AI developer platforms, tenant isolation strategies, capacity planning for shared platforms.
Agent orchestration platforms, MCP tool mesh, A2A agent network design, multi-agent system architecture, agent runtime as platform service, agentic backend depth.
AI observability stacks, eval-first architecture engines, OpenTelemetry for AI platforms, SLI/SLO design at architecture level, telemetry pipeline architecture.
Layered AI security systems, AI governance platforms, security architecture patterns, compliance frameworks (EU AI Act/SOC2/HIPAA), policy-as-code at architecture level.
Enterprise RAG system architecture, vector store federation, knowledge graph integration, RAG observability, RAG eval pipelines, RAG capacity planning.
HA/DR AI platforms, production AI platform capstones, disaster recovery design, multi-region AI architecture, business-continuity for AI systems, enterprise capstone projects.
What you'll ship in production
Core responsibilities this discipline prepares you for.
What you'll ship in production
Core responsibilities this discipline prepares you for.
- 1
Define enterprise GenAI architecture
with proper documentation and governance
- Write Architecture Decision Records (ADRs) for GenAI system design choices with trade-off analysis
- Design reference architectures for common enterprise GenAI use cases
- Create ADRs, design reference architectures, and present trade-off analyses to stakeholders
- 2
Design scalable RAG systems
at enterprise scale
- Architect full RAG stacks: document processing → embedding pipelines → pgvector → hybrid search with reranking
- Design multi-tenant data isolation with embedding pipeline separation and row-level security
- Benchmark RAG systems against enterprise-scale document volumes for throughput and accuracy
- 3
Architect multi-agent systems
with MCP mesh and A2A network topology
- Design MCP mesh architecture for distributed tool access across organizational boundaries
- Plan A2A agent network topologies with lifecycle governance and communication protocols
- Stress-test multi-agent architectures with simulated failure scenarios and cascading fault injection
- 4
Lead PoC development and production rollouts
with model selection and cost estimation
- Compare models across providers with cost-per-request modeling and quality benchmarking
- Build prototype evaluation frameworks with production readiness checklists and go/no-go criteria
- Evaluate models for specific use cases, build cost projections, and create decision frameworks
- 5
Design GenAI governance architecture
— RBAC, audit trails, and compliance
- Build multi-tenant GenAI governance with role-based access control for models, prompts, and data
- Design audit trail architecture with policy-as-code enforcement and compliance reporting
- Architect governance for multi-business-unit enterprises and validate regulatory compliance
- 6
Oversee operational architecture
— observability, FinOps, SLA management
- Design full-stack observability architecture spanning metrics, logs, traces, and LLM-specific telemetry
- Architect FinOps dashboards and incident response workflows with SLA definition and monitoring
- Validate operational architecture designs against production SLA targets and failure scenarios
- 7
Integrate GenAI with enterprise data platforms
— pipelines, knowledge graphs, streaming
- Design data architectures integrating PostgreSQL, pgvector, Kafka streaming, Neo4j, Redis, and MinIO
- Architect data flows that support multiple GenAI use cases simultaneously across shared infrastructure
- Build data architecture designs for multi-use-case enterprise scenarios with isolation and scaling
- 8
Present architecture decisions
with cost/risk analysis to leadership
- Apply ADR methodology with structured trade-off analysis and risk quantification frameworks
- Conduct architecture reviews with stakeholders and defend design decisions under scrutiny
- Write ADRs, conduct architecture reviews, and present cost/risk arguments for design choices
Curriculum
11 courses · each builds on previous goals
Curriculum
11 courses · each builds on previous goals
21 goals unlocked for preview — click to read. Locked goals need a subscription.