What does an AI Infrastructure Engineer do?

An AI Infrastructure Engineer designs the platform layer around AI workloads: model routing, RAG systems, Kubernetes runtime, observability, cost controls, policy, and production operations.

How does GitOps improve Kubernetes operations?

GitOps makes Git the source of truth for Kubernetes state, so deployments, rollbacks, drift detection, and environment changes become reviewable, repeatable, and easier to audit.

What is an AI Gateway?

An AI Gateway is an infrastructure boundary for AI requests. It handles routing, rate limits, provider failover, prompt policy, token budgets, and telemetry before requests reach model providers.

How do you monitor LLM infrastructure?

LLM infrastructure needs request telemetry, token and cost attribution, latency/error SLOs, provider health, prompt-policy signals, and traces that connect AI requests back to services and users.

Technical case studies

AI infrastructure case studies.

Flagship architecture notes covering AI gateways, Kubernetes/EKS, GitOps, RAG, restore automation, and observability. The sitemap intentionally highlights the strongest pages; the supporting inventory stays available for internal linking and AI retrieval.

AI infrastructure hub · Kubernetes GitOps hub

Flagship case studies

Cloud-Native AI Gateway

A production AI gateway turns model access into a governed platform capability: requests enter one boundary, policy is applied consistently, and observability follows every model call.

Result: AI traffic becomes an operable platform flow with visible policy decisions, model routing, cost attribution, and provider resilience.

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Kanister Backup Restore

Application-aware Kubernetes recovery needs more than snapshots. Kanister-style workflows make restore behavior repeatable, testable, and reviewable before an incident.

Result: Restore behavior becomes repeatable, reviewable, and easier to rehearse, reducing pressure during production incidents.

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GitOps ArgoCD Flux

GitOps is not just a deployment tool choice. It is a platform contract for how teams promote, observe, roll back, and audit Kubernetes state.

Result: GitOps decisions become explicit delivery contracts with reviewable promotion, drift visibility, and designed rollback paths.

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RAG Knowledge Platform

A RAG knowledge platform turns repositories, runbooks, architecture notes, and project metadata into retrievable engineering context with citations and safer answer boundaries.

Result: The AI Twin can answer infrastructure questions with scoped project context, source references, and clear knowledge boundaries.

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OpenTelemetry Observability Mesh

An observability mesh connects traces, metrics, logs, runbooks, and ownership so production behavior can be understood from user request to Kubernetes workload.

Result: Operators can move from symptom to owner faster with connected request paths, workload signals, SLO burn, and runbook context.

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Supporting case inventory

Open supporting architecture notes (17)

Automatic SaaS Restore System — Recovery becomes a platform capability instead of an emergency script.
SBOM Integration — Supply-chain visibility becomes part of the delivery system, not a quarterly compliance export.
LLM Infrastructure Runtime — LLM usage becomes a controlled platform capability with observability and operating contracts instead of isolated API calls.
EKS Platform Foundation — Clusters become a repeatable platform product rather than a one-off infrastructure build.
SLO Driven Monitoring — Monitoring shifts from raw infrastructure charts to reliability decisions teams can act on.
Multi Region GitOps — Regional delivery becomes auditable and reversible while keeping infrastructure state understandable.
Terraform Platform Modules — Infrastructure changes become reviewable product changes instead of undocumented console state.
ArgoCD App of Apps — Platform state becomes visible in Git and easier to bootstrap, audit, and recover.
Policy as Code Guardrails — Teams get fast feedback while platform standards are enforced consistently across environments.
Secrets and Certificate Automation — Sensitive material becomes lifecycle-managed infrastructure instead of scattered manual state.
Incident Runbook Automation — Incident response becomes calmer, more repeatable, and easier to improve after the event.
Self Healing Infrastructure — Common failure modes can recover faster while preserving control over high-risk actions.
AI DevOps Labs Platform — Infrastructure knowledge becomes hands-on practice instead of static documentation.
Developer Platform Interface — Teams can ship through clear platform paths while platform engineers keep control of the underlying system.
Cost and Token Observability — Cost becomes an operational signal teams can understand before it becomes a finance surprise.
FleetDM Endpoint Visibility — Endpoint state becomes part of the broader infrastructure picture instead of a separate security island.
Zero Trust Service Mesh — East-west traffic becomes governed, observable, and easier to reason about during security reviews and incidents.

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