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GenAI Solutions Architecture

L6 Staff/Principal · 11 courses · 270 chapters

Design enterprise GenAI reference architectures, create ADRs and technical standards, bridge GenAI with enterprise workflows.

What you'll learn

Core responsibilities this discipline prepares you for.

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

Your learning path

11 courses · sequenced for compounding · 270 chapters

Beginner13 Ch

Foundations

Python Essentials for Agent Builders

Master Python fundamentals from zero to professional code structure. Builds incrementally toward agent-ready patterns.

Intermediate20 Ch

Step 2

LLM Foundations for Agent Builders

Deep understanding of LLM internals, data pipelines, architecture, and multi-provider integration patterns.

Intermediate17 Ch

Step 3

Kubernetes Essentials for GenAI Engineers

Ship GenAI workloads on K8s — pods, services, Helm, GPU scheduling, and production-grade deployment patterns.

Intermediate12 Ch

Step 4

Web APIs & Services for GenAI Engineers

Design, build, and harden HTTP APIs with FastAPI — auth, streaming, rate limiting, OpenAPI contracts.

Intermediate10 Ch

Step 5

Data Infrastructure Essentials for GenAI

Kafka, pgvector, object stores, and data pipelines — the storage spine under every production GenAI system.

Advanced15 Ch

Step 6

GenAI Inference Engineering

Production-grade LLM application development with hosted APIs (Anthropic, OpenAI, Gemini) — retries, fallbacks, caching.

Advanced16 Ch

Step 7

GenAI Agent Engineering

Build production-grade agents with hosted LLMs — planning, tools, memory, evaluation, and orchestration patterns.

Advanced11 Ch

Step 8

Enterprise LLM Customization

Customize LLMs for enterprise — prompt engineering, RAG at scale, fine-tuning, and domain adaptation techniques.

Advanced10 Ch

Step 9

GenAI Operations

Run GenAI in production — monitoring, dunning, incident response, cost control, and the on-call runbook.

Advanced14 Ch

Step 10

GenAI Evaluation, Safety & Governance

Evaluate, red-team, and govern GenAI systems — offline evals, online metrics, safety guardrails, compliance.

Advanced132 Ch

Capstone

GenAI Architecture & Design Patterns

Reference architectures and design patterns for multi-agent, multi-tenant, and regulated GenAI systems.

GenAI stack that you will run labs

Tools and APIs you invoke directly from every lab in this discipline — not the infrastructure GenBodha uses to host them.

LiteLLM

Abstract provider choice in reference stacks

OpenAI SDK

First-party client for GPT-4o labs

Anthropic SDK

First-party client for Claude 4 labs

Google ADK

Build ADK-based agents for Gemini

LangGraph

Reference architectures for agent graphs

LlamaIndex

Structured retrieval for RAG labs

DSPy

Programmatic prompt optimization

RAGAS

Measure RAG quality at scale

NeMo Guardrails

Safety constraints in arch labs

OpenTelemetry

Trace multi-service GenAI systems

Prometheus

Architecture-level metrics in labs

Grafana

Multi-tenant dashboards you design

pgvector

Vector storage in reference stacks

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