Account

GenAI Inference Engineering

L4-L5 · 294h · 7 courses · 98 chapters

Architect multi-provider LLM gateways, implement semantic caching and batch optimization, monitor provider SLAs, and optimize inference costs.

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

What you'll own in this role

Core responsibilities this discipline prepares you for.

Design LLM gateway infrastructure

routing requests across providers

Deploy and configure LiteLLM gateway on Kubernetes with provider routing rules and load balancing
Manage API key rotation, failover policies, and per-provider request distribution
Validate gateway behavior under failover scenarios and measure routing latency overhead

Optimize request latency

through caching, batching, and streaming

Implement semantic caching with Redis using embedding similarity for cache key matching
Build request batching strategies and streaming-first response patterns
Benchmark cache hit rates, measure P50/P95 latency improvements, and tune eviction policies

Implement structured output extraction

from LLMs with type safety

Use Pydantic AI for type-safe LLM interactions with guaranteed schema compliance
Build structured extraction pipelines with Instructor and DSPy for programmatic optimization
Validate extraction accuracy across providers and measure schema conformance rates

Build cost attribution and FinOps dashboards

tracking token spend

Track token costs per team, model, and feature using Langfuse cost attribution
Build Grafana dashboards for cost visualization with Prometheus budget alerting
Implement cost optimization through semantic caching, model tiering, and prompt compression

Monitor inference quality metrics

in production

Instrument LLM calls with OpenTelemetry spans capturing latency, tokens, and error rates
Set up Logfire for Python-native tracing and Prometheus for P50/P95/P99 latency monitoring
Configure alerting rules that detect latency spikes and diagnose root causes from distributed traces

Implement intelligent routing

— route queries to model tiers based on complexity

Build RouteLLM semantic routing with model cascading: cheap models for simple, expensive for complex
Configure complexity-based dispatch logic with fallback chains across providers
Demonstrate 60%+ cost savings while maintaining output quality on standardized test datasets

Manage API rate limits and quotas

across providers

Build rate limiting middleware in FastAPI with per-endpoint and per-user throttling
Configure LiteLLM quota management with per-team token budgets and key rotation policies
Validate graceful degradation behavior under sustained load with provider quota exhaustion

Deploy inference services on K8s

with scaling and health checks

Configure Kubernetes Deployments with readiness/liveness probes tailored for LLM services
Set up Horizontal Pod Autoscaler with custom metrics for token throughput scaling
Validate zero-downtime rolling updates under active inference load

Tools you'll ship with

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

LiteLLMOpenRouterOpenAI APIAnthropic APIGemini APIRedisPrometheusGrafanaK8sFastAPIPostgreSQLLangfuse

Your learning route

7 courses · sequenced for compounding · 98 chapters · ~294 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

GenAI Inference Engineering

15 chapters

Step 6

Enterprise LLM Customization

11 chapters

Step 7 · Capstone

GenAI Operations

10 chapters

Start the GenAI Inference Engineering discipline today

30-day money-back guarantee · cancel anytime on monthly plan

Subscribe — $27/mo (6-month plan) →Or save with a 4-pack bundle →