LLMOps Platform Engineering

Design and operate an internal LLM platform that supports rapid experimentation without compromising reliability, cost, or compliance.

Outcomes

• Standardized path from experiment to production
• Safe model rollout with quality and safety gates
• Repeatable infra modules for inference, vector DB, and observability
• Clear ownership model across platform, app, and security teams

Reference Architecture

1. Control Plane: model registry, prompt/version catalog, policy checks, eval pipeline.
2. Data Plane: inference gateway, vector database, cache, feature store.
3. Ops Plane: telemetry, alerting, SLO dashboards, cost analytics.
4. Security Plane: IAM boundaries, secret rotation, content filters, audit logs.

Golden Delivery Workflow

1. Train/fine-tune or onboard provider model.
2. Register artifact and metadata (license, intended use, constraints).
3. Run automated eval suite (quality + safety + latency + cost).
4. Deploy canary behind gateway with strict traffic policy.
5. Promote after SLO and business KPI thresholds pass.
6. Keep rollback target hot for fast reversion.

CI/CD Design for AI Services

• Build immutable containers with pinned dependencies and model hashes.
• Use environment promotion: dev -> stage -> prod.
• Fail deployment if:
  • regression evals drop below baseline,
  • safety tests exceed risk threshold,
  • p95 latency exceeds SLO budget.
• Store deployment evidence for audits (commit SHA, eval report, approver).

Operational SLOs

• Availability: 99.9% for synchronous inference endpoints.
• Latency: p95 under product-specific target (for example, <1200ms).
• Cost: per-request and per-tenant budget ceilings.
• Quality: task success rate and groundedness thresholds.

Platform Guardrails

• Enforce tenant quotas and model allow-lists.
• Require structured output contracts for automation paths.
• Default to low-risk model settings for critical workflows.
• Disable unconstrained tool execution in production.

Tooling Stack (Example)

• Orchestration: Argo Workflows / GitHub Actions / Airflow.
• Model Registry: MLflow / custom metadata DB.
• Gateway: LiteLLM / Envoy-based API gateway.
• Observability: OpenTelemetry + Prometheus + Grafana + Langfuse.
• Policy: OPA/Rego for deployment and runtime checks.

Incident Readiness

• Runbooks for model outage, provider timeout spikes, and cost surges.
• Chaos drills for provider failover and vector DB degradation.
• Pre-approved rollback path with one-command execution.

Related Skills

• ai-pipeline-orchestration - Orchestrate ingestion and inference workflows
• agent-evals - Build evaluation gates for releases
• llm-gateway - Route and control LLM traffic