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GenAI Platform Engineering

L5-L6 · 339h · 8 courses · 113 chapters

Build internal GenAI developer platforms with self-service capabilities, multi-tenancy, RBAC, CI/CD for model/prompt/guardrail pipelines.

Role-alignedHands-on labsCapstone project30-day money-back

What you'll own in this role

Core responsibilities this discipline prepares you for.

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

Tools you'll ship with

Industry-standard stack for current L4–L6 GenAI engineering roles.

K8sHelmKustomizeArgoCDGitHub ActionsTerraformFastAPIPostgreSQLRedisKafkaPrometheusGrafanaLiteLLM

Your learning route

8 courses · sequenced for compounding · 113 chapters · ~339 hours

Step 1 · Foundations

Python Essentials for Agent Builders

13 chapters

Step 2

LLM Foundations for Agent Builders

20 chapters

Step 3

Kubernetes Essentials for GenAI Engineers

17 chapters

Step 4

Web APIs & Services for GenAI Engineers

12 chapters

Step 5

Data Infrastructure Essentials for GenAI

10 chapters

Step 6

DevOps Foundations for GenAI Engineers

10 chapters

Step 7

GenAI Operations

10 chapters

Step 8 · Capstone

AI Developer Platform Engineering

21 chapters

Start the GenAI Platform Engineering discipline today

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