GenAI Platform Engineering
Build internal GenAI developer platforms with self-service capabilities, multi-tenancy, RBAC, CI/CD for model/prompt/guardrail pipelines.
What a GenAI platform engineer does · GenAI Platform Engineering
When a company has more than one team building with language models, someone has to build the road they all drive on. That is this role: an internal platform where a team can get a tenant, a gateway key, a vector store, an eval gate and a deploy pipeline without filing a ticket. The tools are the infrastructure canon — Kubernetes, Helm, Kustomize, ArgoCD, GitHub Actions, Terraform — applied to GenAI-specific problems: autoscaling for bursty token workloads, multi-tenant isolation, CI/CD for prompts and guardrails as well as code, and observability that connects a slow answer to a pod. Inference engineering tunes the request path on top of your platform; engineering leaders budget for it.
Start with these 3 free GenAI Platform Engineering lessons
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9 courses, each building on the last. The bar shows each course's share of the curriculum. 58 lessons are free to read.
Python Essentials for Agent Builders 7.8% of the curriculum
In scope for GenAI Platform Engineering: chapters 1–13 — 62 of this path’s 795 lessons. 1 free GenAI Platform Engineering lesson
LLM Foundations for Agent Builders 6.9% of the curriculum
In scope for GenAI Platform Engineering: chapters 1–6, 14, 17–20 — 55 of this path’s 795 lessons. 1 free GenAI Platform Engineering lesson
Kubernetes Essentials for GenAI Engineers 9.1% of the curriculum
In scope for GenAI Platform Engineering: chapters 1–12 — 72 of this path’s 795 lessons. 1 free GenAI Platform Engineering lesson
Web APIs & Services for GenAI Engineers 7.5% of the curriculum
In scope for GenAI Platform Engineering: chapters 1–10 — 60 of this path’s 795 lessons. 1 free GenAI Platform Engineering lesson
Data Infrastructure Essentials for GenAI 7.5% of the curriculum
- PostgreSQL & pgvectorInstall pgvector and create vector-enabled tablesIntegrate pgvector with SQLAlchemy ORM
- Advanced PostgreSQL
- Redis for Caching & SessionsUse Redis pub/sub for real-time event broadcastingBuild rate limiting with Redis sorted setsMonitor Redis performance and memory
- MinIO Object Storage
- Kafka FundamentalsMonitor Kafka with consumer lag metrics
- Event-Driven Architectures
- Data Pipeline OrchestrationDefine Argo Workflow templates for data processing
- Data Quality & ValidationImplement embedding quality checks
- Data Infrastructure OperationsDeploy data services on Kubernetes with StatefulSetsConfigure Prometheus monitoring for data servicesImplement automated PostgreSQL backup and restore
DevOps Foundations for GenAI Engineers 6.0% of the curriculum
- Git Workflows for AI Teams
- CI Pipelines with GitHub ActionsCompare CI platforms: GitHub Actions vs Tekton vs DaggerConfigure CI to run on GKE self-hosted runners
- Container Image CI/CDBuild optimized Docker images for AI applicationsAutomate image builds with GitHub ActionsSign images with Cosign and enforce Binary Authorization on GKEBuild multi-architecture images for GKE
- ArgoCD & GitOpsInstall ArgoCD and deploy first applicationCompare GitOps controllers: ArgoCD ApplicationSet vs Flux CDImplement ArgoCD RBAC and multi-tenancy
- Infrastructure as CodeBuild Helm charts with Skaffold local development workflowUse Kustomize overlays for environment managementEnforce policies with OPA Gatekeeper and test with ConftestTest Helm charts before deployment
- Secrets Management
- Deployment StrategiesImplement blue-green and progressive delivery with Argo Rollouts
- DevOps for AI ArtifactsImplement evaluation-gated deployment
GenAI Operations 37.7% of the curriculum
In scope for GenAI Platform Engineering: chapters 1–37, 50–52, 56–65 — 300 of this path’s 795 lessons.
GenAI Evaluation, Safety & Governance 2.3% of the curriculum
In scope for GenAI Platform Engineering: chapters 20–21, 24 — 18 of this path’s 795 lessons.
AI Developer Platform Engineering 15.1% of the curriculum
- Internal Developer Platform VisionDesign service catalog data model and golden path templatesBuild service catalog REST API with search and filteringIntegrate platform with Kubernetes cluster discoveryBuild platform health dashboard with Prometheus metricsDeploy platform control plane with Helm and ArgoCD
- Platform API & Service MeshIntegrate service mesh with Kubernetes endpoints
- Developer Self-Service Portal
- LLM Gateway as Platform ServiceAdd request logging with PII redaction pipelineMonitor gateway latency and token usage with Prometheus
- Model Registry & Catalog
- Multi-Tenant ArchitectureImplement K8s namespace provisioning with quota enforcementDeploy multi-tenant infrastructure with Helm overrides
- RBAC & Access ControlDesign RBAC model with roles, permissions, and scopesDeploy RBAC with policy-as-code validation
- Resource Quota Management
- Cost Allocation & ChargebackBuild cost tracking pipeline from gateway metricsDeploy cost dashboards with Grafana
- Onboarding AutomationDeploy onboarding system with ArgoCD integration
- Agent Runtime as Platform ServiceDesign agent execution model with sandboxed podsBuild agent job submission and scheduling APIBuild agent runtime auto-scaling and queue depth metricsDeploy agent runtime with K8s Job controller
- Tool Registry & MCP HubDesign tool registry model with MCP server metadataDeploy MCP hub with Helm and agent integration
- Vector DB as Platform ServiceDeploy managed pgvector with Helm StatefulSet
- Evaluation Platform ServiceDeploy evaluation platform with Helm and Grafana
- Prompt Engineering WorkspaceBuild prompt approval workflow with evaluation gate
- Platform Monitoring & SLAsDeploy SLA monitoring with Grafana dashboards
- Compliance & Audit
- Platform Change ManagementDeploy change management with ArgoCD hooks
- Platform Analytics & AdoptionDeploy analytics pipeline with Grafana dashboards
- AI Platform CapstoneDeploy complete platform with Helm umbrella chart
Core responsibilities this discipline prepares you for. Open one to see how you build it.
Build the internal GenAI platform enabling developers to deploy LLM applications self-service
- Design platform APIs with golden path templates and self-service provisioning workflows
- Build developer portals with pre-approved LLM configurations, guardrails, and monitoring included
- Wire end-to-end self-service: from app registration to deployed inference endpoint with observability
Design multi-tenant infrastructure with namespace isolation and RBAC
- Implement Kubernetes namespace isolation with RBAC policies and resource quotas per tenant
- Automate tenant provisioning with network policies and admission controllers
- Validate tenant isolation by enforcing resource limits under concurrent multi-team workloads
Implement CI/CD pipelines with GitOps for GenAI applications
- Set up ArgoCD GitOps for declarative deployment from Git push to production rollout
- Build GitHub Actions workflows with act for local CI and Helm chart packaging
- Wire complete GitOps pipelines with Kustomize overlays for dev/staging/production environments
Manage data infrastructure — databases, caches, message queues on K8s
- Deploy PostgreSQL + pgvector, Redis, Kafka, Neo4j, and MinIO as Kubernetes-native services
- Configure backup/restore, horizontal scaling, and monitoring for each data component
- Benchmark throughput and failover behavior for each infrastructure component under load
Build autoscaling for GenAI workloads using event-driven scaling and batch job queuing
- Configure KEDA for event-driven pod autoscaling based on queue depth, HTTP rate, and custom metrics
- Set up Kueue for Kubernetes-native batch job scheduling with priorities and fair quotas
- Validate auto-scaling policies under burst GenAI workloads with realistic traffic patterns
Provision infrastructure-as-code using K8s-native tooling
- Declare infrastructure as Kubernetes custom resources with Crossplane providers
- Manage databases, storage, and networking declaratively through kubectl apply
- Verify reconciliation behavior by modifying infrastructure state and observing self-healing
Implement full-stack observability across the GenAI platform
- Build unified observability with Prometheus metrics, Grafana dashboards, and OpenTelemetry tracing
- Add Logfire for Python application tracing and Langfuse for LLM-specific cost and quality monitoring
- Wire a unified observability stack spanning infrastructure, application, and LLM inference layers
Operate LLM gateways as platform infrastructure
- Manage LiteLLM gateway operations: API key lifecycle, per-team cost tracking, and provider health
- Handle model version migration and zero-downtime provider switching
- Operate a production gateway serving multiple internal teams with isolated quotas and routing
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.
Build the paved road: a self-service internal developer platform — portals, service catalogs, templates and scaffolding, and self-service provisioning so other engineers ship AI without filing tickets.
Make one platform safely serve many teams: multi-tenant architecture, namespace and resource isolation, noisy-neighbor prevention, per-tenant limits, SSO/identity, RBAC, and secrets-as-a-service.
Build the deployment control plane others consume: a self-service paved road for promoting models, prompts, and guardrails, with registry/versioning and GitOps offered as a platform capability — not operating one team's pipeline.
Engineer the platform layer on Kubernetes: operators, CRDs and controllers, the control-plane backend behind self-service, multi-cluster, GPU/accelerator scheduling, and platform IaC (Crossplane/Terraform).
Offer model serving as a platform product: provision inference endpoints and model-serving-as-a-service, internal model gateways, and autoscaling serving infra that tenants request on demand.
Give every tenant observability out of the box: per-tenant dashboards, traces, and metrics the platform provides, the shared stack teams plug into, and fleet-wide plus platform-self SLOs.
Enforce the rules platform-wide: policy-as-code and admission policies (OPA/Kyverno), an audit engine every tenant inherits, and governance gates baked into the control plane — preventive, not operational compliance.
Run the platform's economics: per-tenant cost attribution, chargeback/showback, cross-tenant quota enforcement, and capacity planning — billing and bounding tenants, not reducing model spend.
Host eval as a self-serve capability: shared eval/benchmark harnesses, golden-set storage, and gate scheduling that tenants plug their own metrics into — the infrastructure, not the eval science.
Provision data infra as a platform offering: managed vector stores and data-infra golden paths tenants self-serve — not building the corpus/embedding pipelines themselves.
Baseline provider access in platform code: LLM/embedding SDK calls, auth, and retries.
Production Python for platform code: async, typing, Pydantic, and error handling.