GenAI Solutions Architecture
Design enterprise GenAI reference architectures, create ADRs and technical standards, bridge GenAI with enterprise workflows.
Verifiable skill graph
10 skill groups · each becomes a signed node on your graph.
Verifiable skill graph
10 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.
Authoring enterprise reference architectures and Architecture Decision Records: canonical ADRs with rejected alternatives and explicit tradeoffs, constraint-justified decisions, failure-mode reasoning. The role's headline deliverable — design judgment, not implementation.
Designing GenAI solution architectures and patterns: compound-AI topologies, agentic/multi-agent architecture, multi-provider gateway/routing, orchestration — chosen with pattern-selection rationale and failure-mode analysis. Absorbs the former gateway and agent-mesh bars.
Technology-selection frameworks and build-vs-buy: vendor evaluation, weighted-criteria decision matrices, lock-in/exit-cost and TCO analysis for the GenAI stack. The reusable apparatus for making architecture decisions — distinct from authoring a single ADR.
Designing for production at scale: high-availability and disaster-recovery architecture, resilience, blast-radius isolation, cell-based and multi-tenant platform design, and the prototype-to-production transition. Absorbs the former cell-based/multi-tenancy bar.
Governance, trust-boundary and data-residency architecture: layered security architecture, policy-as-code, compliance and risk frameworks at the architecture level. The enterprise buyer's risk surface — design, not control implementation.
Bridging GenAI with enterprise workflows and data: integration architecture, enterprise data boundaries, system-of-record integration, event/API integration patterns.
Observability and eval-first architecture: designing traceability, SLOs, eval gates and feedback loops as first-class architecture concerns rather than afterthoughts.
Enterprise RAG architecture: retrieval governance, data-boundary-aware retrieval, hybrid retrieval at scale, and knowledge architecture for enterprise corpora. A deep, separately-hireable competency.
Provider SDK integration: client setup, multi-provider calls, streaming, error handling. Prerequisite plumbing.
Production-grade Python for solutions architects: prototyping, reference implementations, scripting. Prerequisite.
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
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