GenAI Inference Engineering
Architect multi-provider LLM gateways, implement semantic caching and batch optimization, monitor provider SLAs, and optimize inference costs.
What a GenAI inference engineer does · GenAI Inference Engineering
Every request to a language model is a decision about money, latency and risk, and inference engineering is the discipline that makes those decisions deliberately. You put a gateway in front of the providers — LiteLLM or OpenRouter, routed by cost, quality and quota — add semantic caching in Redis so repeated questions never pay twice, batch and stream where it helps, and extract structured output with type guarantees instead of hoping. Prometheus and Grafana carry the FinOps view: which team, model and feature spent what, and whether the provider kept its SLA. This is the serving side of the stack that platform engineering builds and LLMOps operates; here you own the request path itself.
Start with these 3 free GenAI Inference Engineering lessons
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7 courses, each building on the last. The bar shows each course's share of the curriculum. 16 lessons are free to read.
Python Essentials for Agent Builders 10.1% of the curriculum
In scope for GenAI Inference Engineering: chapters 1–13 — 62 of this path’s 614 lessons. 1 free GenAI Inference Engineering lesson
LLM Foundations for Agent Builders 15.0% of the curriculum
In scope for GenAI Inference Engineering: chapters 1–16, 19–20 — 92 of this path’s 614 lessons. 1 free GenAI Inference Engineering lesson
Kubernetes Essentials for GenAI Engineers 11.7% of the curriculum
In scope for GenAI Inference Engineering: chapters 1–12 — 72 of this path’s 614 lessons. 1 free GenAI Inference Engineering lesson
Web APIs & Services for GenAI Engineers 9.8% of the curriculum
In scope for GenAI Inference Engineering: chapters 1–10 — 60 of this path’s 614 lessons. 1 free GenAI Inference Engineering lesson
GenAI Inference Engineering 17.3% of the curriculum
- Production Hosted LLM ArchitectureProvider Landscape AnalysisToken EconomicsRate Limit Management
- Prompt Caching Mastery
- Batch API Strategies
- Model Routing and Selection
- Structured Outputs
- Function Calling and Tool Orchestration
- 6 of 18 chapters shownand 12 more
Enterprise LLM Customization 5.9% of the curriculum
- LiteLLM Gateway
- Smart Router
- Provider Caching Comparison
- FinOps Controller
- Multi-Provider Reliability & Scaling
- Streaming & Real-Time AI
GenAI Operations 30.3% of the curriculum
In scope for GenAI Inference Engineering: chapters 1–6, 14–16, 20–25 and 16 more — 186 of this path’s 614 lessons. 9 free GenAI Inference Engineering lessons
Core responsibilities this discipline prepares you for. Open one to see how you build it.
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
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.
Design and operate LLM gateways: routing across providers, request hedging, circuit breakers, failover chains, latency-based routing, rate-limit middleware, and provider SLA/health tracking that drives routing. The LiteLLM / OpenRouter layer.
Optimize inference spend: token counting and per-provider cost, prompt-caching APIs (Anthropic cache_control, OpenAI prefix, Gemini context), batch APIs, semantic caching, model tiering/cascading, and cache-friendly prompt structure.
Measure and improve request latency: TTFT, p50/p95/p99 profiling, SSE/token streaming, streaming-vs-blocking trade-offs, throughput tuning, prefix-cache reasoning, and latency-driven model selection.
Keep a multi-provider gateway within provider limits: TPM/RPM ceilings, token-bucket admission control, backpressure and queueing under burst, 429 budgeting, multi-key/account/region capacity planning, and load/capacity testing.
Treat the gateway as the security boundary: PII detection/redaction before third-party egress, prompt-injection and jailbreak filtering, output moderation/guardrails, per-tenant key isolation and secrets, audit logging, and data-residency/zero-retention routing.
Make model/provider swaps safe: offline eval harnesses, golden-set regression tests, canary and shadow traffic on swaps, and A/B quality comparison gating routing decisions.
Instrument LLM calls with OpenTelemetry, Langfuse, Logfire, and Prometheus: latency/error/cost telemetry, production sampling, and signal emission. (Quality adjudication lives in Eval & Swap-Safety.)
Enforce typed outputs and reliable tool calling: JSON mode, Pydantic-validated schemas, function calling, multi-tool orchestration, parallel tool calls, and error recovery on malformed outputs.
Apply reasoning models (o-series, extended thinking), test-time compute (CoT/ToT/best-of-N), context-window and KV/prefix-cache economics, and reasoning-token budget trade-offs.
Call OpenAI, Anthropic, and Gemini SDKs in production: auth, retry with backoff, response parsing, error recovery, and unified multi-provider interfaces.
Production-grade Python applied to inference engineering: async/await, Pydantic models, dataclasses, decorators, context managers, generators, pytest. The language fluency required to ship LLM-backed services.
Ship the gateway as a service: containerize, deploy, autoscale on RPS/latency (HPA), and manage secrets/config for a stateless I/O-bound LLM proxy.