GenAI Solutions Architecture
Design enterprise GenAI reference architectures, create ADRs and technical standards, bridge GenAI with enterprise workflows.
What a GenAI solutions architect does · GenAI Solutions Architecture
The architect is the person who decides how a company builds with language models before ten teams decide differently. You write the reference architectures and the ADRs behind them, choose between RAG designs and multi-agent patterns for a given workload, set the standards for gateways, evaluation and governance, and connect all of it to the enterprise data platforms that already exist. This is the broadest curriculum of the twelve — eleven courses spanning agents, inference, data, platform, security and delivery — because the role has to be conversant in each before it can arbitrate between them. You also lead proofs of concept into production and present the decision to the people who fund it. Platform engineering builds what you specify; delivery leads ship it for clients.
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11 courses, each building on the last. The bar shows each course's share of the curriculum. 36 lessons are free to read.
Python Essentials for Agent Builders 3.8% of the curriculum
In scope for GenAI Solutions Architecture: chapters 1–13 — 62 of this path’s 1635 lessons. 1 free GenAI Solutions Architecture lesson
LLM Foundations for Agent Builders 5.3% of the curriculum
In scope for GenAI Solutions Architecture: chapters 1–10, 14–20 — 87 of this path’s 1635 lessons. 1 free GenAI Solutions Architecture lesson
Kubernetes Essentials for GenAI Engineers 4.4% of the curriculum
In scope for GenAI Solutions Architecture: chapters 1–12 — 72 of this path’s 1635 lessons. 1 free GenAI Solutions Architecture lesson
Web APIs & Services for GenAI Engineers 3.7% of the curriculum
In scope for GenAI Solutions Architecture: chapters 1–10 — 60 of this path’s 1635 lessons. 1 free GenAI Solutions Architecture lesson
Data Infrastructure Essentials for GenAI 3.7% of the curriculum
- Neo4j Graph Database
- Data Pipeline Orchestration
- Data Quality & Validation
- Data Infrastructure Operations
- 4 of 10 chapters shownand 6 more
GenAI Inference Engineering 6.5% of the curriculum
- Reasoning Models
- Test-Time Compute Strategies
- Agentic Patterns
- Context and Memory Management
- Reliability and Resilience
- Cost Optimization and Observability
- 6 of 18 chapters shownand 12 more
GenAI Agent Engineering 19.1% of the curriculum
In scope for GenAI Solutions Architecture: chapters 11–59, 61, 70 — 312 of this path’s 1635 lessons.
Enterprise LLM Customization 17.2% of the curriculum
- Enterprise Data Pipeline
- Synthetic Data Factory
- Fine-Tuned Enterprise Model
- RLVR-Trained Reasoning Model
- Reward Engineering Toolkit
- Model Eval DashboardBuild eval suite versioning and managementBuild eval regression root cause analyzer
- DSPy Enterprise ModulesBuild 5 enterprise DSPy modules
- Langfuse ObservabilityBuild custom eval pipelines in Langfuse
- Agent Mesh: A2A + ADKBuild A2A agent mesh
- Guardrails PipelineBuild guardrails pipelineTest guardrails under adversarial inputBenchmark guardrail performance
- 10 of 47 chapters shownand 37 more
GenAI Operations 23.9% of the curriculum
- GenAI Failure Catalog
- GenAI SLI Framework
- GenAI SLO Engine
- GenAI Toil Analyzer
- GenAI Launch Readiness
- GenAI Runbook Engine
- 6 of 65 chapters shownand 59 more
GenAI Evaluation, Safety & Governance 2.9% of the curriculum
In scope for GenAI Solutions Architecture: chapters 1, 4, 8 and 5 more — 48 of this path’s 1635 lessons.
GenAI Architecture & Design Patterns 9.5% of the curriculum
- GenAI ADR EngineBuild ADR schema and decision taxonomy for GenAI technology choicesValidate ADR decisions against production telemetryImplement ADR recommendation engine using historical outcomes
- Reference Architecture Registry
- Compound AI System Designer
- Eval-First Architecture EngineBuild eval gate component with pluggable evaluator registryMeasure eval gate effectiveness with precision-recall trackingCreate eval architecture audit report with coverage analysis
- Cost-Optimized AI Router
- AI Traffic GatewayImplement Istio service mesh for AI microservice communication
- AI Observability StackBuild OpenTelemetry instrumentation for multi-step AI pipelines
- Layered AI Security SystemIntegrate NeMo Guardrails and LlamaFirewall for multi-layer defense
- MCP Tool MeshBuild MCP server registry with capability discovery and health checksValidate MCP tool composition correctness and safetyBuild MCP tool routing with load balancing and failoverCreate MCP ecosystem governance dashboard
- A2A Agent NetworkBuild A2A agent card registry with capability advertisementImplement A2A task delegation with streaming artifact exchangeValidate A2A communication reliability with failure injectionBuild A2A agent trust and authorization frameworkOptimize A2A network topology for latency and reliabilityCreate A2A network operations dashboard with federation view
- Event-Driven AI ProcessorImplement event backbone with Redis Streams for AI workloads
- Agent Orchestration PlatformBuild agent pool manager with lifecycle and capability registrationValidate orchestration correctness with agent trajectory evaluation
- AI Data ArchitectureBuild unified data access layer across vector, graph, and relational stores
- Multi-Tenant AI PlatformImplement tenant-aware routing with per-tenant model and guardrail config
- 14 of 26 chapters shownand 12 more
Core responsibilities this discipline prepares you for. Open one to see how you build it.
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
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
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
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
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
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
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
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
Each group becomes a signed node on your public skill graph — a recruiter-auditable record of the labs you actually passed. Share the URL; they see the discipline, the labs, and the signature.
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.