LLMOps Engineering
Monitor hallucination rates and token costs, operate guardrails and eval gates, manage prompt versioning and canary deployments.
What a LLMOps engineer does · LLMOps Engineering
A language-model system that worked at launch drifts: the provider changes a model, a prompt edit ships without a test, hallucination creeps up on one document type, and the token bill doubles before anyone notices. LLMOps is the practice of catching all of that early. You build continuous evaluation pipelines and eval gates in CI, canary prompt deployments with ArgoCD and Argo Workflows, hallucination and cost monitoring in Prometheus, Grafana, OpenTelemetry and Langfuse, and capacity plans for traffic that arrives in bursts. Prompt-attack detection and RAG data quality are yours too. Inference engineering designs the gateway you run; safety and evaluation designs the benchmarks you gate on.
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8 courses, each building on the last. The bar shows each course's share of the curriculum. 18 lessons are free to read.
Python Essentials for Agent Builders 7.1% of the curriculum
In scope for LLMOps Engineering: chapters 1–13 — 62 of this path’s 869 lessons. 1 free LLMOps Engineering lesson
LLM Foundations for Agent Builders 10.0% of the curriculum
In scope for LLMOps Engineering: chapters 1–10, 14–20 — 87 of this path’s 869 lessons. 1 free LLMOps Engineering lesson
Kubernetes Essentials for GenAI Engineers 8.3% of the curriculum
In scope for LLMOps Engineering: chapters 1–12 — 72 of this path’s 869 lessons. 1 free LLMOps Engineering lesson
Web APIs & Services for GenAI Engineers 6.9% of the curriculum
In scope for LLMOps Engineering: chapters 1–10 — 60 of this path’s 869 lessons. 1 free LLMOps Engineering lesson
DevOps Foundations for GenAI Engineers 5.5% of the curriculum
- Git Workflows for AI TeamsImplement trunk-based development for AI projectsImplement pre-commit hooks and automated dependency updates
- Deployment Strategies
- DevOps for AI Artifacts
- 3 of 8 chapters shownand 5 more
Enterprise LLM Customization 8.3% of the curriculum
- Guardrails Pipeline
- Prompt Injection Red Team
- Compliance Audit Trail
- Compliance Test Suite
- MLOps for LLM Applications
- Advanced Evaluation & Observability
- 6 of 12 chapters shownand 6 more
GenAI Evaluation, Safety & Governance 9.0% of the curriculum
In scope for LLMOps Engineering: chapters 4, 8, 10–15 and 5 more — 78 of this path’s 869 lessons.
GenAI Operations 44.9% of the curriculum
- GenAI Helm Charts
- Dev/Staging/Prod Environments
- GitOps Control Plane
- GenAI CI/CD PipelinesBuild Argo Workflow templates for GenAI artifact CI/CD
- GenAI Secret ManagerBuild secret sync monitoring and alerting for rotation compliance
- Self-Service Environment API
- Eval Gate PipelineImplement Eval Gates in Argo Workflows That Block Promotion on Failure
- Embedding Pipeline OpsDeploy pgvector and build embedding ingestion pipeline with operational monitoring
- Vector Index OpsImplement pgvector index maintenance with VACUUM and reindexing schedulesDeploy Qdrant and compare operational characteristics with pgvector
- RAG Quality MonitorCompare retrieval quality across embedding models with Cohere Rerank
- Data Quality OpsBuild completeness checks for embedding coverage and knowledge graph gaps
- Injection Monitoring SystemImplement multi-layer prompt injection detection with pattern and embedding-based methods
- Guardrail Operations PlatformDeploy Guardrails AI and LlamaFirewall on K8s for runtime content validationImplement hot-reload guardrail configuration without service restarts
- Multi-Tenant GenAI PlatformAutomate tenant onboarding with namespace provisioning and secret management
- 14 of 65 chapters shownand 51 more
Core responsibilities this discipline prepares you for. Open one to see how you build it.
Design CI/CD pipelines for LLM application deployment
- Build ArgoCD GitOps workflows with Helm-based deployments and environment promotion
- Implement canary and blue-green rollout strategies with automated quality-based rollback
- Wire complete CI/CD pipelines that trigger rollbacks when evaluation metrics degrade
Monitor LLM systems in production — latency, errors, costs, quality
- Instrument with OpenTelemetry and Langfuse v3 for OTEL-native distributed tracing
- Build Grafana dashboards with Logfire for Python application monitoring and alerting
- Set up monitoring stacks that detect anomalies, fire alerts, and enable trace-based root cause analysis
Manage LLM gateway operations — key rotation, failover, quota management
- Operate LiteLLM gateway: API key lifecycle management, provider health monitoring, per-team quotas
- Handle zero-downtime model version switching with traffic draining and validation
- Simulate provider outages and quota exhaustion to validate failover and degradation behavior
Implement FinOps practices — cost attribution, budgets, and optimization
- Track token costs by team, feature, and model with Prometheus-based budget alerting
- Implement cost optimization through semantic caching, model tiering, and prompt compression
- Build FinOps dashboards that demonstrate measurable cost reduction across optimization strategies
Build continuous evaluation pipelines for production LLM quality
- Run RAGAS and DeepEval evaluation pipelines alongside production traffic as shadow evaluators
- Set up Langfuse-based quality tracking with automated quality gates and threshold alerting
- Detect quality degradation in real time and trigger automated alerts when scores drop below baselines
Detect and respond to prompt attacks and safety incidents in production
- Monitor NeMo Guardrails operationally for prompt injection and jailbreak detection patterns
- Classify incident severity and execute structured response workflows with containment procedures
- Simulate attack scenarios end-to-end: detection, triage, remediation, and post-incident analysis
Manage data quality for RAG systems — freshness, drift, accuracy
- Monitor embedding drift and retrieval accuracy with continuous RAGAS evaluation
- Set up automated reindexing triggers and stale content detection pipelines
- Build monitoring for live RAG systems that detects quality degradation and triggers reindexing workflows
Implement capacity planning — predict demand and right-size deployments
- Forecast token demand using historical usage patterns and run load tests for LLM services
- Model SLA capacity requirements and configure KEDA-based autoscaling policies
- Run load tests that predict capacity requirements and validate SLA compliance under variable traffic
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.
Is the answer good? Hallucination/groundedness detection, eval-gate pipelines that block bad deploys, quality-drift detection, RAGAS/DeepEval, LLM-as-judge (and validating the judge), and RAG retrieval-quality monitoring.
Keep it cheap: per-request cost attribution, token-budget controllers and gates, cache economics, model-tier arbitrage, batch scheduling, and capacity forecasting — driving spend down (per-tenant chargeback belongs to the platform engineer).
Keep it not-on-fire: incident command, triage and post-mortems, runbook automation, on-call and alerting hygiene, error-budget policy, and provider-outage failover/fallback drills.
Keep it compliant: audit engines and trails, EU AI Act / SOC2 / HIPAA / GDPR controls, policy gates, bias and fairness monitoring, and regulatory reporting.
Safely change what's in prod: prompt/model versioning and registry, provenance/lineage, canary analysis, automated rollback triggers, progressive delivery, feature flags, and shadow/replay testing.
Is the service up, fast, and within SLO? OpenTelemetry tracing, Langfuse, Prometheus/Grafana, latency/throughput/error telemetry, and SLI/SLO engines.
Operate the safety layer in production: runtime guardrails with canary/kill-switch, key rotation, PII detection, prompt-injection monitoring, content-safety filters, and red-team automation.
Ship safely and repeatably: GitHub Actions/ArgoCD GitOps, Infrastructure-as-Code, secrets management, environment promotion, and deployment automation.
Operate the serving substrate: Helm, multi-tenant isolation and quota, HPA, gateway/guardrail sidecars, and namespace policy.
Keep the retrieval substrate up in production: health checks, failure detection, recovery, and capacity/staleness alarms — operating it, not building it.
Baseline LLM access in ops code: provider SDKs, multi-provider gateways, smart routers, and provider failover.
Production Python for ops tooling: async/await, Pydantic, typing, dataclasses, pytest, and error handling.